Systems and methods for fleet management

ABSTRACT

A system for electric aircraft fleet management for at least an electric aircraft is provided. the system includes a computing device communicatively connected to at least an electric aircraft, wherein the computing device is configured to receive a plurality of measured aircraft operation datum from a sensor disposed on the at least an electric aircraft, select a training set as a function of each measured aircraft operation datum of the plurality of measured aircraft operation datum and the at least an electric aircraft, wherein each measured aircraft operation datum of the plurality of measured aircraft operation datum is correlated to an element of modeled aircraft data, and generate, using a machine-learning algorithm, an aircraft performance model output based on the plurality of measured aircraft operation datum and the selected training set, wherein generating an aircraft performance model includes generating a performance alert.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Nonprovisional application Ser.No. 17/406,353 filed on Aug. 19, 2021, and entitled “SYSTEMS AND METHODSFOR FLEET MANAGEMENT,” the entirety of which is incorporated herein byreference.

FIELD OF THE INVENTION

The present invention generally relates to the field of aircraft fleetmanagement. In particular, the present invention is directed to a systemfor electric aircraft fleet management.

BACKGROUND

The operation of an electric aircraft in of itself requires meticulousoversight. Operation of a fleet of electric aircraft can involve eachelectric aircraft to be used for various purposes such as commercialfight or cargo shipment. Management of the operation of a fleet ofelectric aircraft that also involves consumers can be difficult forflight managers.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for electric aircraft fleet management for atleast an electric aircraft is provided. The system includes a computingdevice communicatively connected to at least an electric aircraft. Thecomputing device is remote to the at least an electric aircraft. Thecomputing device configured to authenticate the at least an aircraftusing a credential received from the at least an electric aircraft. Thecomputing device is configured to receive a plurality of measuredaircraft operation datum from a sensor disposed on the at least anelectric aircraft. The computing device is configured to select atraining set as a function of each measured aircraft operation datum ofthe plurality of measured aircraft operation datum and the at least anelectric aircraft, where each measured aircraft operation datum of theplurality of measured aircraft operation datum is correlated to anelement of modeled aircraft data. The computing device is configured togenerate, using a machine-learning algorithm, an aircraft performancemodel output based on the plurality of measured aircraft operation datumand the selected training set, wherein generating an aircraftperformance model includes generating a performance alert.

In another aspect, a method for electric aircraft fleet management forat least an electric aircraft is provided. The method includesreceiving, by a computing device remote to at least electric aircraftand communicatively connected to the at least an electric aircraft, aplurality of measured aircraft operation datum from a sensor disposed onthe at least an electric aircraft, selecting a training set as afunction of each measured aircraft operation datum of the plurality ofmeasured aircraft operation datum and the at least an electric aircraft,wherein each measured aircraft operation datum of the plurality ofmeasured aircraft operation datum is correlated to an element of modeledaircraft data, and generating, using a machine-learning algorithm, anaircraft performance model output based on the plurality of measuredaircraft operation datum and the selected training set, whereingenerating an aircraft performance model includes generating aperformance alert.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an exemplary embodiment of a system for afleet management system for at least an electric aircraft;

FIG. 2 is a block diagram of an exemplary embodiment of a scheduledatabase;

FIG. 3 is a block diagram of an exemplary embodiment of an aircraftperformance model output;

FIG. 4 is a block diagram illustrating an embodiment of anauthentication module;

FIG. 5 is a block diagram illustrating an embodiment of anauthentication database;

FIG. 6 is a block diagram illustrating an embodiment of a biometricdatabase;

FIG. 7 is a block diagram illustrating an embodiment of a flightmanagement engine;

FIG. 8 is a block diagram illustrating an embodiment of a registrymodule;

FIG. 9 is a block diagram illustrating an embodiment a maneuverdatabase;

FIG. 10 is a block diagram illustrating an embodiment of a schedulermodule;

FIG. 11 is a block diagram illustrating an embodiment of an aircraftmarker database;

FIG. 12 is a block diagram illustrating an embodiment of a pilot logdatabase;

FIG. 13 is a schematic representation illustrating an embodiment of aclustering unsupervised machine-learning model;

FIG. 14 is a schematic representation illustrating an embodiment of asupervised machine-learning model;

FIG. 15 is a flow diagram illustrating a method for fleet managementsystem;

FIG. 16 is a diagrammatic representation of an exemplary embodiment ofan electric aircraft;

FIG. 17 is a block diagram of an exemplary embodiment of a flightcontroller;

FIG. 18 is a block diagram of an exemplary embodiment of amachine-learning module; and

FIG. 19 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. As used herein, the word “exemplary” or “illustrative” means“serving as an example, instance, or illustration.” Any implementationdescribed herein as “exemplary” or “illustrative” is not necessarily tobe construed as preferred or advantageous over other implementations.All of the implementations described below are exemplary implementationsprovided to enable persons skilled in the art to make or use theembodiments of the disclosure and are not intended to limit the scope ofthe disclosure, which is defined by the claims. Furthermore, there is nointention to be bound by any expressed or implied theory presented inthe preceding technical field, background, brief summary or thefollowing detailed description. It is also to be understood that thespecific devices and processes illustrated in the attached drawings, anddescribed in the following specification, are simply exemplaryembodiments of the inventive concepts defined in the appended claims.Hence, specific dimensions and other physical characteristics relatingto the embodiments disclosed herein are not to be considered aslimiting, unless the claims expressly state otherwise.

At a high level, aspects of the present disclosure are directed tosystems for electric aircraft fleet management by which an aircraftfleet manager may use to view logistical information describing a fleetof electric aircraft. Aspects of the present disclosure can also be usedby consumers or customers who own or use electric aircraft to viewlogistical information of the electric aircraft corresponding to thecustomer or user. Exemplary embodiments illustrating aspects of thepresent disclosure are described below in the context of severalspecific examples.

Aspects of the present disclosure can be used by both consumers andoperators to view logistical information including pilot and customerinputs regarding the quality of a flight experience. The system of thepresent disclosure may allow for a fleet manager to resolve humanerrors, issues, or complaints and view a variety of visualrepresentations of logistical information regarding the flight of anelectric aircraft and the number of repairs performed on an electricaircraft.

Aspects of the present disclosure can be used to verify specificelectric aircraft by any infrastructure that may allow for the landingand take-off of an electric aircraft. A recharging station may allow anelectric aircraft in the air to transmit data to the recharging stationbefore and/or after authenticating the electric aircraft before it landson the recharging station.

Aspects of the present disclosure can be used to authenticate users andallow users who have authority over at least an electric aircraft or afleet of electric aircraft to view a simulation of the flight paths ofeach electric aircraft, conduct commercial applications with the atleast an electric aircraft, etc.

For purposes of this disclosure, in aviation, an “instrument approach,”instrument approach plan or instrument approach procedure (IAP) is aseries of predetermined maneuvers for the orderly transfer of anaircraft operating under instrument flight rules from the beginning ofthe initial approach to a landing or to a point from which a landing maybe made visually. Instrument flight rules (IFR) is one of two sets ofregulations governing all aspects of civil aviation aircraft operations;the other is visual flight rules (VFR). The U.S. Federal AviationAdministration's (FAA) Instrument Flying Handbook defines IFR as: “Rulesand regulations established by the FAA to govern flight under conditionsin which flight by outside visual reference is not safe. IFR flightdepends upon flying by reference to instruments in the flight deck, andnavigation is accomplished by reference to electronic signals.” It isalso a term used by pilots and controllers to indicate the type offlight plan an aircraft is flying, such as an IFR or VFR flight plan.

Aspects of the present disclosure can assist with and/or substitute forair traffic control (ATC) instrument approach for electric aircraftseeking to verify or confirm a proposed or potential flight plan.Typically, instrument flight plane pilots provide information such astype of aircraft, start and departure airport, end airport, current paththey want to fly (low/high altitude airways), safety information (peopleon board, equipment and the like) which is filed through a centralgovernment system. Any central or local ATC receives a copy of theintended flight plan.

Referring now to FIG. 1 , a block diagram of an exemplary embodiment ofa system 100 for a fleet management system for at least an electricaircraft is illustrated. The system 100 includes a flight controller 120that is communicatively connected to a sensor 104 which is disposed onthe at least an electric aircraft. Sensor 104 may be disposed on atleast a flight component of an electric aircraft. In a non-limitingembodiment, a sensor 104 may be coupled to an electric aircraft such asthe nose of the electric aircraft to capture a wider area of the outsideenvironment. In a non-limiting embodiment, sensor 104 may be coupled toa plurality of flight components such as a landing gear, a propulsor, anenergy source, a motor, and the like. “Communicatively connected,” forthe purposes of this disclosure, refers to two or more componentselectrically, or otherwise connected and configured to transmit andreceive signals from one another. Signals may include electrical,electromagnetic, visual, audio, radio waves, or another undisclosedsignal type alone or in combination. Flight controller 120 is configuredto receive a plurality of measured aircraft operation datum from thesensor 104. A “measured aircraft operation datum,” for the purpose ofthis disclosure, is an element of data describing the components thatfactor into the operation of an aircraft. In a non-limiting embodiment,measured aircraft operation datum may include a plurality of histories,records, projections, and the like thereof, regarding the operation ofan aircraft. The plurality of measured aircraft operation datum mayinclude a plurality of records, reports, logs, and the like thereof,describing the performance history of the at least an electric aircraft.plurality of measured aircraft operation datum may include informationdescribing, but not limited to, electric aircraft personnel, electricaircraft capabilities, and the like thereof. Any datum or signal hereinmay include an electrical signal. Electrical signals may include analogsignals, digital signals, periodic or aperiodic signal, step signals,unit impulse signal, unit ramp signal, unit parabolic signal, signumfunction, exponential signal, rectangular signal, triangular signal,sinusoidal signal, sinc function, or pulse width modulated signal.Sensor 104 may include circuitry, computing devices, electroniccomponents or a combination thereof that translates a plurality of datuminto at least an electronic signal configured to be transmitted toanother electronic component. Plurality of measured aircraft operationdatum may include information describing the maintenance, repair, andoverhaul of an electric aircraft or an electric aircraft's flightcomponents. plurality of measured aircraft operation datum may include arecord of maintenance activities and their results including a pluralityof tests, measurements, replacements, adjustments, repairs, and thelike, which may be intended to retain and/or restore a functional unitof an electric aircraft. plurality of measured aircraft operation datummay include a record of data of, but not limited to, functional checks,servicing, repairing or replacing of necessary devices, equipment,machinery, and the like, pertaining to an electric aircraft. In anon-limiting embodiment, the plurality of measured aircraft operationdatum may include a unique identification number denoting a part of anelectric aircraft that was installed, repaired, or replaced as afunction of an aircraft maintenance. In a non-limiting embodiment, theplurality of measured aircraft operation datum may include a record ofmaintenance and/or repair schedules corresponding to an electricaircraft. The plurality of measured aircraft operation datum may includea record of potential maintenance and repair schedules corresponding toan electric aircraft. A “maintenance schedule,” for the purposes of thisdisclosure, refer to an appointment reserved for an aircraft for amaintenance or repair to be conducted upon. A person of ordinary skillin the art, after viewing the entirety of this disclosure, wouldappreciate the various elements of data pertaining to a record of datain the context of maintenance and repair.

With continued reference to FIG. 1 , “sensor,” for the purposes of thisdisclosure, refer to a computing device configured to detect, capture,measure, or combination thereof, a plurality of external and electricvehicle component quantities. Sensor 104 may be integrated and/orconnected to at least an actuator, a portion thereof, or anysubcomponent thereof. Sensor 104 may include circuitry or electroniccomponents configured to digitize, transform, or otherwise manipulateelectrical signals. Electrical signals may include analog signals,digital signals, periodic or aperiodic signal, step signals, unitimpulse signal, unit ramp signal, unit parabolic signal, signumfunction, exponential signal, rectangular signal, triangular signal,sinusoidal signal, sinc function, or pulse width modulated signal. Theplurality of datum captured by sensor 104 may include circuitry,computing devices, electronic components or a combination thereof thattranslates into at least an electronic signal configured to betransmitted to another electronic component.

With continued reference to FIG. 1 , sensor 104 may include a pluralityof sensors in the form of individual sensors or a sensor suite workingin tandem or individually. A sensor suite may include a plurality ofindependent sensors, as described herein, where any number of thedescribed sensors may be used to detect any number of physical orelectrical quantities associated with an aircraft power system or anelectrical energy storage system. Independent sensors may includeseparate sensors measuring physical or electrical quantities that may bepowered by and/or in communication with circuits independently, whereeach may signal sensor output to a control circuit such as a usergraphical interface. In an embodiment, use of a plurality of independentsensors may result in redundancy configured to employ more than onesensor that measures the same phenomenon, those sensors being of thesame type, a combination of, or another type of sensor not disclosed, sothat in the event one sensor fails, the ability to detect phenomenon ismaintained and in a non-limiting example, a user alter aircraft usagepursuant to sensor readings. Sensor may be configured to detect pilotinput from at least pilot control. At least pilot control may include athrottle lever, inceptor stick, collective pitch control, steeringwheel, brake pedals, pedal controls, toggles, joystick. One of ordinaryskill in the art, upon reading the entirety of this disclosure wouldappreciate the variety of. Collective pitch control may be consistentwith disclosure of collective pitch control in U.S. patent applicationSer. No. 16/929,206 and titled “HOVER AND THRUST CONTROL ASSEMBLY FORDUAL-MODE AIRCRAFT”, which is incorporated herein by reference in itsentirety.

With continued reference to FIG. 1 , sensor 104 may include a motionsensor. A “motion sensor,” for the purposes of this disclosure is adevice or component configured to detect physical movement of an objector grouping of objects. One of ordinary skill in the art wouldappreciate, after reviewing the entirety of this disclosure, that motionmay include a plurality of types including but not limited to: spinning,rotating, oscillating, gyrating, jumping, sliding, reciprocating, or thelike. Sensor 104 may include, but not limited to, torque sensor,gyroscope, accelerometer, magnetometer, inertial measurement unit (IMU),pressure sensor, force sensor, proximity sensor, displacement sensor,vibration sensor, LIDAR sensor, and the like. In a non-limitingembodiment sensor 104 ranges may include a technique for the measuringof distances or slant range from an observer including sensor 104 to atarget which may include a plurality of outside parameters. “Outsideparameter,” for the purposes of this disclosure, refer to environmentalfactors or physical electric vehicle factors including health statusthat may be further be captured by a sensor 104. Outside parameter mayinclude, but not limited to air density, air speed, true airspeed,relative airspeed, temperature, humidity level, and weather conditions,among others. Outside parameter may include velocity and/or speed in aplurality of ranges and direction such as vertical speed, horizontalspeed, changes in angle or rates of change in angles like pitch rate,roll rate, yaw rate, or a combination thereof, among others. Outsideparameter may further include physical factors of the components of theelectric aircraft itself including, but not limited to, remaining fuelor battery. Outside parameter may include at least an environmentalparameter. Environmental parameter may be any environmentally basedperformance parameter as disclosed herein. Environment parameter mayinclude, without limitation, time, pressure, temperature, air density,altitude, gravity, humidity level, airspeed, angle of attack, anddebris, among others. Environmental parameters may be stored in anysuitable datastore consistent with this disclosure. Environmentalparameters may include latitude and longitude, as well as any otherenvironmental condition that may affect the landing of an electricaircraft. Technique may include the use of active range finding methodswhich may include, but not limited to, light detection and ranging(LIDAR), radar, sonar, ultrasonic range finding, and the like. In anon-limiting embodiment, sensor 104 may include at least a LIDAR systemto measure ranges including variable distances from the sensor 104 to apotential landing zone or flight path. LIDAR systems may include, butnot limited to, a laser, at least a phased array, at least amicroelectromechanical machine, at least a scanner and/or optic, aphotodetector, a specialized GPS receiver, and the like. In anon-limiting embodiment, sensor 104 including a LIDAR system may targean object with a laser and measure the time for at least a reflectedlight to return to the LIDAR system. LIDAR may also be used to makedigital 4-D representations of areas on the earth's surface and oceanbottom, due to differences in laser return times, and by varying laserwavelengths. In a non-limiting embodiment the LIDAR system may include atopographic LIDAR and a bathymetric LIDAR, wherein the topographic LIDARthat may use near-infrared laser to map a plot of a land or surfacerepresenting a potential landing zone or potential flight path while thebathymetric LIDAR may use water-penetrating green light to measureseafloor and various water level elevations within and/or surroundingthe potential landing zone. In a non-limiting embodiment, electricaircraft may use at least a LIDAR system as a means of obstacledetection and avoidance to navigate safely through environments to reacha potential landing zone. Sensor 104 may include a sensor suite whichmay include a plurality of sensors that may detect similar or uniquephenomena. For example, in a non-limiting embodiment, sensor suite mayinclude a plurality of accelerometers, a mixture of accelerometers andgyroscopes, or a mixture of an accelerometer, gyroscope, and torquesensor.

With continued reference to FIG. 1 , sensor 104 is configured to detecta plurality of measured aircraft operation datum including aircraftcomponent state data 108, a payload data 112, and a pilot data 116. A“component state data,” for the purposes of this disclosure, refer toany datum that represents the status or health status of a flightcomponent or any component of an electric aircraft. The component statedata 108 of a plurality of flight components. “Flight components,” forthe purposes of this disclosure, includes components related to, andmechanically connected to an aircraft that manipulates a fluid medium inorder to propel and maneuver the aircraft through the fluid medium. Theoperation of the aircraft through the fluid medium will be discussed atgreater length hereinbelow. Aircraft component state data 108 mayinclude a plurality of state information of a plurality of aircraftcomponents of the electric aircraft. A state information of theplurality of state information of the plurality of aircraft componentsmay include an aircraft flight duration, a distance of the aircraftflight, a plurality of distances of an aircraft from the surface, andthe like. The component state data 108 may denote a location of theaircraft, status of the aircraft such as health and/or functionality,aircraft flight time, aircraft on frame time, and the like thereof.Component state data 108 may include aircraft logistics of an electricaircraft of a plurality of electrical aircraft. An “aircraft logistics,”for the purposes of this disclosure, refer to a collection of datumrepresenting any detailed organization and implementation of anoperation of an electric aircraft. In a non-limiting embodiment,aircraft logistics may include unique identification numbers assigned toeach electric aircraft. In a non-limiting embodiment, aircraft logisticsmay include a historical record of locations corresponding to anelectric aircraft that may represent the aircraft's destination orpotential destination. Aircraft logistics may include time an electricaircraft was in the air and a historical record of the different rate ofvelocity the aircraft may have commanded. Aircraft component state data108 may include a history of health information of an electric aircraft.In a non-limiting embodiment, a history of an electric aircraft's healthmay be measured with the ability to be presented in a visual format to auser. Component state data 108 may include potential health data orpotential data of electric aircraft and/or electric aircraft parts thatmay be incorporated on to an electric aircraft. A person of ordinaryskill in the art, after viewing the entirety of this disclosure, wouldappreciate the type of data measured in the context of aircraftlogistics.

With continued reference to FIG. 1 , sensor 104 is configured to detecta plurality of measured aircraft operation datum including a payloaddata 112. A “payload data,” for the purposes of this disclosure, referto any datum that describes the cargo of an electric aircraft. payloaddata 112 may include information describing the logistics or aircraftlogistics of a commercial application of the at least an electricaircraft. In a non-limiting embodiment, payload data 112 may includeinformation about, but not limited to, the delivery location, the pickuplocation, the type of package and/or cargo, the priority or the package,and the like thereof. A person of ordinary skill in the art, afterviewing the entirety of this disclosure, would appreciate the multitudeof information for a payload data.

With continued reference to FIG. 1 , sensor 104 is configured to detecta plurality of measured aircraft operation datum including a pilot data116. A “pilot data,” for the purposes of this disclosure, refer to anydatum that represent a state of information of a pilot of an electricaircraft. Pilot data 116 may include any datum that refers to at leastan element of data identifying and/or a pilot input or command. At leastpilot control may be communicatively connected to any other componentpresented in system, the communicative connection may include redundantconnections configured to safeguard against single-point failure. Pilotinput may indicate a pilot's desire to change the heading or trim of anelectric aircraft. Pilot input may indicate a pilot's desire to changean aircraft's pitch, roll, yaw, or throttle. Aircraft trajectory ismanipulated by one or more control surfaces and propulsors working aloneor in tandem consistent with the entirety of this disclosure,hereinbelow. Pitch, roll, and yaw may be used to describe an aircraft'sattitude and/or heading, as they correspond to three separate anddistinct axes about which the aircraft may rotate with an appliedmoment, torque, and/or other force applied to at least a portion of anaircraft. Pilot data 116 may include any information describing themovement and actions of the pilot during a flight. In a non-limitingembodiment, pilot data 116 may record any buttons or electricalcomponent that the pilot may have completed an action upon. The recordof the actions may be used by the flight controller 120 to map a flightsimulation which may include a general simulation of the pilot's actionsand movements. A person of ordinary skill in the art, after viewing theentirety of this disclosure, would appreciate the monitoring and mappingof a pilot's movements and actions in the context of simulation.

With continued reference to FIG. 1 , the pilot data 116 may include apilot input and/or pilot control that may include an electrical. Anypilot input as described herein may be consistent with any pilot inputas described in U.S. patent application Ser. No. 17/218,387 filed onMar. 31, 2021, and titled, “METHOD AND SYSTEM FOR FLY-BY-WIRE FLIGHTCONTROL CONFIGURED FOR USE IN ELECTRIC AIRCRAFT,” which is incorporatedherein in its entirety by reference. Pilot input may include a pilotcontrol which may include a throttle wherein the throttle may be anythrottle as described herein, and in non-limiting examples, may includepedals, sticks, levers, buttons, dials, touch screens, one or morecomputing devices, and the like. Additionally, a right-handfloor-mounted lift lever may be used to control the amount of thrustprovided by the lift fans or other propulsors. The rotation of a thumbwheel pusher throttle may be mounted on the end of this lever and maycontrol the amount of torque provided by the pusher motor, or one ormore other propulsors, alone or in combination. Any throttle asdescribed herein may be consistent with any throttle described in U.S.patent application Ser. No. 16/929,206 filed on Jul. 15, 2020, andtitled, “A HOVER AND THRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT”,which is incorporated herein in its entirety by reference. Sensor 104may be mechanically and communicatively connected to an inceptor stick.The pilot input may include a left-hand strain-gauge style STICK for thecontrol of roll, pitch and yaw in both forward and assisted lift flight.A 4-way hat switch on top of the left-hand stick enables the pilot toset roll and pitch trim. Any inceptor stick described herein may beconsistent with any inceptor or directional control as described in U.S.patent application Ser. No. 17/001,845 filed on Aug. 25, 2020, andtitled, “A HOVER AND THRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT”,which is incorporated herein in its entirety by reference.

With continued reference to FIG. 1 , pilot data 116 may include aplurality of pilot tracking elements. A “pilot tracking element,” forthe purposes of this disclosure, refer to any datum that represents apiece of data describing a pilot of an aircraft. In a non-limitingembodiment, a pilot tracking element may include a health status of thepilot, alert status of the pilot, experience level, and the likethereof. Pilot tracking element may include age of the pilot, a customerrating of the pilot, and the like thereof. In a non-limiting embodiment,a customer or passenger may use the fleet management system 100 toretrieve information regarding the pilot of an electric aircraft thecustomer may be a passenger of or be the owner of A person of ordinaryskill in the art, after viewing the entirety of this disclosure, wouldappreciate the plurality of information concerning the pilot in thecontext of a fleet management system.

With continued reference to FIG. 1 , the flight controller 120 isconfigured to receive the plurality of measured aircraft operation datumfrom the sensor 104. Flight controller 120 may include a computingdevice, wherein the computing device is described in further detailbelow. Flight controller 120 further may include a consumer tool whichis configured to be used for commercial applications. A “consumer tool,”for the purposes of this disclosure, refer to a computer program that isused to facilitate commercial applications. Commercial applications mayinclude, but not limited to, delivery of cargo, transportation ofpassengers, and the like thereof. Consumer tool may include a loadcalculation, optimization of package deliveries, and the like thereof.Consumer tool is further configured, in part, to generate an aircraftperformance model output 136. The system 100 may configure the flightcontroller 120 to integrate a plurality of consumer tools into thesystem and at least a user device 148. In a non-limiting embodiment,consumer tool may be used to store, retrieve, and view a plurality ofinformation regarding the logistics of commercial applications. In anon-limiting embodiment, a customer may use the consumer tool tooptimize the delivery of packages and/or cargo. For example, theconsumer tool may perform various optimization algorithms andcalculations to complete a delivery or flight request. A person ofordinary skill in the art, after viewing the entirety of thisdisclosure, would appreciate the purpose of a consumer tool in thecontext of commercial applications. The flight controller 120 mayinclude at least a hooking procedure configured to integrate additionalfunctionalities with existing subsystems of the flight controller 120including a consumer tool. A “hooking procedure,” for the purposes ofthis disclosure, refer to a plurality of techniques used to alter thebehavior of operating systems, applications, and other softwarecomponents. In a non-limiting embodiment, the flight controller 120 maybe configured to perform debugging and extending functionality of asubsystem including a consumer tool. In a non-limiting embodiment, theflight controller 120 may intercept user input such as a keyboard ormouse event from the consumer tool before the consumer tool may be usedto impact the generating of an aircraft performance model output 136and/or a machine-learning algorithm 132. In a non-limiting embodiment,flight controller 120 may perform benchmarking programs to measurequantifiable information of existing systems such as the consumer tool.A person of ordinary skill in the art, after viewing the entirety ofthis disclosure, would appreciate the range of techniques used toaugment existing applications into the overall system in the context ofmanagement.

With continued reference to FIG. 1 , flight controller 120 is configuredto select a training set 124 as a function of the plurality of measuredaircraft operation datum. The training set includes the plurality ofmeasured aircraft operation datum correlated to an element of modeledaircraft data. An “element of modeled aircraft data,” for the purpose ofthis disclosure, is a virtual representation of an aircraft event orphenomenon. Element of modeled aircraft data may include a virtualrepresentation. In a non-limiting embodiment, element of modeledaircraft data may include a visualization of a history and/or records ofthe plurality of measured aircraft operation datum. The element ofmodeled aircraft data may include a simulation of a flight. Selectingthe training data may be performed utilizing any means of selection asdescribed in the entirety of this disclosure. In a non-limitingembodiment, selecting a training set 124 may include correlating theplurality of measured aircraft operation datum to an aircraftperformance model output 136 which may include a health projection forat least a flight component associated. A “health projection,” for thepurpose of this disclosure, is a predictive model of the health statusof at least a flight component of a plurality of flight components of anelectric aircraft. In a non-limiting embodiment, selecting training set124 may include correlating the plurality of measured aircraft operationdatum to a health history for at least a flight component. A “healthhistory,” for the purpose of this disclosure, is a record and/or modeldepicting a history of the health status of the at least a flightcomponent of a plurality of flight components. In a non-limitingembodiment, the health history may include the health of at least aflight component since an authentication of an electric aircraft by thesystem 190. In a non-limiting embodiment, health history may be inputtedby a user.

With continued reference to FIG. 1 , flight controller 120 may beconfigured to operate a flight simulator 128. A “flight simulator” is aprogram or set of operations that simulate flight. In some cases, aflight simulator may simulate flight within an environment, for examplean environmental atmosphere in which aircraft fly, airports at whichaircraft take-off and land, and/or mountains and other hazards aircraftattempt to avoid crashing into. In some cases, an environment mayinclude geographical, atmospheric, and/or biological features. Forinstance and without limitation, flight simulator may be consistent withdisclosure of flight simulator in U.S. patent application Ser. No.17/348,916 and titled “METHODS AND SYSTEMS FOR SIMULATED OPERATION OF ANELECTRIC VERTICAL TAKE-OFF AND LANDING (EVTOL) AIRCRAFT,” which isincorporated herein by reference in its entirety. In some cases, aflight simulator 128 may model an artificial and/or virtual aircraft inflight as well as an environment in which the artificial and/or virtualaircraft flies. In some cases, a flight simulator 128 may include one ormore physics models, which represent analytically or through data-based,such as without limitation machine-learning processes, physicalphenomenon. Physical phenomenon may be associated with an aircraftand/or an environment. For example, some versions of a flight simulator128 may include thermal models representing aircraft components by wayof thermal modeling. Thermal modeling techniques may, in some cases,include analytical representation of one or more of convective heartransfer (for example by way of Newton's Law of Cooling), conductiveheat transfer (for example by way of Fourier conduction), radiative heattransfer, and/or advective heat transfer. In some cases, flightsimulator 128 may include models representing fluid dynamics. Forexample, in some embodiments, flight simulator may include arepresentation of turbulence, wind shear, air density, cloud,precipitation, and the like. In some embodiments, flight simulator 128may include at least a model representing optical phenomenon. Forexample, flight simulator may include optical models representative oftransmission, reflectance, occlusion, absorption, attenuation, andscatter. Flight simulator 128 may include non-analytical modelingmethods; for example, the flight simulator may include, withoutlimitation, a Monte Carlo model for simulating optical scatter within aturbid medium, for example clouds. In some embodiments, a flightsimulator 128 may represent Newtonian physics, for example motion,pressures, forces, moments, and the like. A person of ordinary skill inthe art, after viewing the entirety of this disclosure, would appreciatethe embodiment that may result from a flight simulation in the contextof fleet management.

With continued reference to FIG. 1 , flight simulator 128 With continuedreference to FIG. 1 , flight controller 120 may be configured tosimulate at least a virtual representation 140. Flight similar 128 maygenerate at least a virtual representation 140 and transmit it to anaircraft performance model output 136. Aircraft performance model output136 may receive a virtual representation 140 instead as a function of amachine-learning algorithm 132 trained by training set 124. As describedin this disclosure, a “virtual representation” includes any model orsimulation accessible by computing device which is representative of aphysical phenomenon, for example without limitation at least an aircraftcomponent. For instance and without limitation, virtual representationmay be consistent with virtual representation in U.S. patent applicationSer. No. 17/348,916 title “METHODS AND SYSTEMS FOR SIMULATED OPERATIONOF AN ELECTRIC VERTICAL TAKE-OFF AND LANDING (EVTOL) AIRCRAFT,” which isincorporated by reference in its entirety. In some cases, virtualrepresentation may be interactive with flight simulator 128. Forexample, in some cases, data may originate from virtual representationand be input into flight simulator 128. Alternatively or additionally,in some cases, virtual representation 140 may modify or transform dataalready available to flight simulator 128. Virtual representation 140may include an aircraft digital twin 140 of at least an aircraftcomponent 116. Aircraft digital twin 140 may include any digital twin asdescribed in this disclosure, for example below. In some cases, at leastan aircraft component includes an electric vertical take-off and landing(eVTOL) aircraft, for example a functional flight-worthy e aircraft; andaircraft digital twin 140 is a digital twin of the eVTOL aircraft. Insome cases, at least a virtual representation 140 may include a virtualcontroller area network. For instance and without limitation, virtualcontroller area network may be consistent with disclosure of virtualcontroller area network in U.S. patent application Ser. No. 17/218,342titled “METHOD AND SYSTEM FOR VIRTUALIZING A PLURALITY OF CONTROLLERAREA NETWORK BUS UNITS COMMUNICATIVELY CONNECTED TO AN AIRCRAFT,” whichis incorporated by reference in its entirety.

With continued reference to FIG. 1 , flight controller 120 is configuredto generate an aircraft performance model output 136 as a function ofthe plurality of measured aircraft operation datum from the sensor and amachine-learning algorithm 132. An “aircraft performance model output,”for the purpose of this disclosure, is an analytical and/or interactivevisualization regarding aircraft operation and/or performancecapabilities. In a non-limiting embodiment, aircraft performance modeloutput 136 may include dashboards and reports configured to bemanipulated by a user. The aircraft performance model output 136 may begenerated as a function of at least a consumer tool. The aircraftperformance model output 136 is described in further detail in FIG. 3 .In a non-limiting embodiment, the aircraft performance model output 136may be identified as a function of a consumer tool wherein the tool mayinclude load calculations, optimization of package deliveries, aplatform for communication of user inputs including complaints, and thelike thereof. The aircraft performance model output 136 may beconfigured to allow for user interaction. The aircraft performance modeloutput 136 may include a plurality of visual information that may beconfigured to be viewed and/or modified by a user via user device suchas a user device 148 as a function of a flight management control 148program.

With continued reference to FIG. 1 , aircraft performance model output136 may be configured to generate and/or include a virtualrepresentation 140. Virtual representation may include any virtualrepresentation as described herein. In a non-limiting embodiment,virtual representation 140 may include a flight simulation of aplurality of flight simulations of a history of past flights of anelectric aircraft. Aircraft performance model output 136 may include avirtual representation of an action of a plurality of commercialapplications. For instance, virtual representation may include athree-dimensional and/or two-dimensional video simulation of the processof a commercial application performed by an electric aircraft, which maybe viewed, modified, configured, or combination thereof, by a userdevice 148. Aircraft performance model output may include a virtualrepresentation of passenger and/or personnel related complains, issues,and the like. For instance, the virtual representation may depict amoment or issue that occurred by humans in an electric aircraft during aflight. In a non-limiting embodiment, a user may use a user device 148to audit complaints, noise complaints for low altitude, pilot complaintsand/or issues, and the like. In a non-limiting embodiment, aircraftperformance model output 136 may include a plurality of histories,records, and schedules regarding the health status, maintenance,quality, and the like thereof, of the components of an electricaircraft. Aircraft performance model output 136 may further beconfigured to include a health projection for the at least a flightcomponent associated with each measured aircraft operation datum of theplurality of measured aircraft operation datum. Aircraft performancemodel output 136 may be further configured to include a health historyfor the at least a flight component associated with each measuredaircraft operation datum of the plurality of measured aircraft operationdatum. A person of ordinary skill in the art, after viewing the entiretyof this disclosure, would appreciate the multitude of aircraftinformation content of the aircraft performance model output.

With continued reference to FIG. 1 , aircraft performance model output136 includes a performance alert 148. A “performance alert,” for thepurpose of this disclosure, is a predictive warning sign indicating anissue. Performance alert 148 may include a warning sign indicatingincrement weather, unusually high turbulence, a flight highway withconsiderable traffic, and the like thereof. In a non-limitingembodiment, performance alert 148 may be depicted in a graphical format.Performance alert 148 may include a plurality of colors, sizes, andassociated sounds configured to attract the attention of a user, pilot,and the like thereof. Performance alert 148 may include a warning signfor any instance of an issue. For example, a new and/or unexpected issuemay occur during a flight regarding a flight component, an electricaircraft personnel, pilot, passenger, cargo, authentication, and thelike thereof. In a non-limiting embodiment, flight controller 120 may beconfigured to record a plurality of data during an occurrence of anissue or during an occurrence of a predictive issue that may occur. In anon-limiting embodiment, performance alert 146 may include a pluralityof performance alerts for each major flight component. For instance, aperformance alert 146 may flash and alert a user during an occurrence ofa failure or degradation of a flight component during flight, beforetakeoff, after landing, and the like thereof. In a non-limitingembodiment, performance alert 146 may be triggered when an obstructiveoutside parameter is detected. A person of ordinary skill in the art,after viewing the entirety of this disclosure, would appreciate thefunction of an alert in the context of detecting, avoiding, andanalyzing unexpected and potentially hazardous obstacles and/or issues.

With continued reference to FIG. flight controller 120 is configured togenerate an aircraft performance model 136 as a function of amachine-learning algorithm 132 based on the plurality of measuredaircraft operation datum and the selected training set 124.Machine-learning algorithm, without limitation, a linear discriminantanalysis. Machine-learning algorithm may include quadratic discriminateanalysis. Machine-learning algorithms may include kernel ridgeregression. Machine-learning algorithms may include support vectormachines, including without limitation support vectorclassification-based regression processes. Machine-learning algorithmsmay include stochastic gradient descent algorithms, includingclassification and regression algorithms based on stochastic gradientdescent. Machine-learning algorithms may include nearest neighborsalgorithms. Machine-learning algorithms may include Gaussian processessuch as Gaussian Process Regression. Machine-learning algorithms mayinclude cross-decomposition algorithms, including partial least squaresand/or canonical correlation analysis. Machine-learning algorithms mayinclude naïve Bayes methods. Machine-learning algorithms may includealgorithms based on decision trees, such as decision tree classificationor regression algorithms. Machine-learning algorithms may includeensemble methods such as bagging meta-estimator, forest of randomizedtress, AdaBoost, gradient tree boosting, and/or voting classifiermethods. Machine-learning algorithms may include neural net algorithms,including convolutional neural net processes.

With continued reference to FIG. 1 , flight controller 120 mayconfigured generate an aircraft performance model output 136 as afunction of the plurality of measured aircraft operation datum and amachine-learning algorithm 132. Machine-learning model may include anaircraft performance model output machine-learning model. Aircraftperformance model output 136 may be generated as a function of a user.Generating the aircraft performance model output 136 may includegenerating a aircraft performance model output 136 training data usingthe plurality of measured data detected by the sensor 104 wherein theplurality of measured aircraft operation datum includes the aircraftcomponent state data 108, the payload data 112, and the pilot data 116,and training an aircraft performance model output machine-learning modelwith the aircraft performance model output 136 training data thatincludes a plurality of data entries wherein each entry correlates theplurality of measured data to a plurality of aircraft performance modeloutputs 136 which may be retrieved from a schedule database, andgenerating the aircraft performance model output 136 as a function ofthe aircraft performance model output machine-learning model and theplurality of measured aircraft operation datum. Aircraft performancemodel output machine-learning model may generate aircraft performancemodel output 136 using alternatively or additional artificialintelligence methods, including without limitation by creating anartificial neural network, such as a convolutional neural networkcomprising an input layer of nodes, one or more intermediate layers, andan output layer of nodes. Connections between nodes may be created viathe process of “training” the network. in which elements from a trainingdataset are applied to the input nodes. a suitable training algorithm(such as Levenberg-Marquardt. conjugate gradient, simulated annealing,or other algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Continuing in reference to FIG. 1 , aircraft performance model outputtraining data may include any aircraft component state data 108, thepayload data 112, and the pilot data 116 data, as described above,organized into training data, as described herein. Such training datamay include a plurality of data entries of schedules and/or flight plansand any data describing the schedules and/or flight plans. Training datamay originate as analysis from previous flights and/or flight plans ofthe electric aircraft, previous flights and/or flight plans of differentelectric aircraft distinct from one another, and the like, from one ormore electric aircraft. Training data may originate as analysis fromprevious schedules of maintenance and/or repair, schedules of flightplans including passenger flight and/or deliveries, and the likethereof. Aircraft performance model output training data may originatefrom one or more electric aircraft pilots, air traffic control (ATC)operators, customer and/or consumers, electric aircraft owners, and thelike thereof, via a user interface such as a user device 148 and atleast a consumer tool communicatively connected with a flight controllerto provide flight history, schedule history, pilot history, air traffichistory, weather condition information, and the like thereof. In anon-limiting embodiment, Flight controller 120 may receive training datafor training aircraft performance model output machine-learning model.It is important to note that training data for machine-learningprocesses, algorithms, and/or models used herein may originate from anysource described for aircraft performance model output 136 trainingdata.

Continuing in reference to FIG. 1 , an aircraft performance model outputmachine-learning model may include any machine-learning algorithm suchas K-nearest neighbors algorithm, a lazy naïve Bayes algorithm, and thelike, machine-learning process such as supervised machine-learning,unsupervised machine-learning, or method such as neural nets, deeplearning, and the like, as described in further detail below. Aircraftperformance model output machine-learning model may be trained to derivean algorithm, function, series of equations, or any mathematicaloperation, relationship, or heuristic, which can automatedly accept aninput of plurality of measured aircraft operation datum and generate anoutput of an aircraft performance model output 136. Aircraft performancemodel output 136 machine-learning algorithm 132 may derive individualfunctions describing unique relationships observed from the aircraftperformance model output 136 training data for each aircraft componentstate data 108, payload data 112, and pilot data 116, wherein differentrelationships may emerge between different pilots, electric aircraft,type of cargo and/or number of passengers in an electric aircraft,flight priority of an electric aircraft, and the like. aircraftperformance model output machine-learning model may derive relationshipsfrom the training data which indicate patterns in estimated flightduration of different flight plans or proposed flight plans according towhere an electric aircraft is departing from and/or arriving to, and thelike. Aircraft performance model output 136 may include any number ofparameters, numerical values, strings, functions, mathematicalexpressions, text, and the like. Aircraft performance model output 136and at least a schedule database 200 may become increasingly morecomplete, and more robust, with larger sets of plurality of measuredaircraft operation datum.

With continued reference to FIG. 1 , flight controller 120 may includean authentication module 144. Authentication module 144 may include anysuitable software and/or hardware as described in the entirety of thisdisclosure. In an embodiment, authentication module 144 and/or computingdevice 104 is configured to authenticate at least an electric aircraft152 and/or user device 148. Authentication module 144 may include alogin portal for users to submit credentials. Authentication module 144and/or computing device 104 may be configured to receive a credentialassociated with a user and/or electric aircraft 152 from at least anelectric aircraft 152 and/or user device 148, compare the credentialfrom at least an electric aircraft 152 and/or user device 148 to anauthorized credential stored within an authentication database, andbypass authentication for at least an electric aircraft 152 and/or userdevice 148 based on the comparison of the credential from at least anelectric aircraft 152 and/or user device 148 to the authorizedcredential stored within the authentication database. A “credential” asdescribed in the entirety of this disclosure, is a datum representing anidentity, attribute, code, and/or characteristic specific to a userand/or user device. For example, and without limitation, the credentialmay include a username and password unique to the user and/or userdevice. The username and password may include any alpha-numericcharacter, letter case, and/or special character. As a further exampleand without limitation, the credential may include a digitalcertificate, such as a PKI certificate. At least an electric aircraft152 and/or user device 148 may include an additional computing device,such as a mobile device, laptop, desktop computer, or the like; as anon-limiting example, the at least an electric aircraft 152 and/or userdevice 148 may be a computer and/or smart phone operated by apilot-in-training at an airport hangar. At least an electric aircraft152 and/or user device 148 may include, without limitation, a display incommunication with computing device 104; the display may include anydisplay as described in the entirety of this disclosure such as a lightemitting diode (LED) screen, liquid crystal display (LCD), organic LED,cathode ray tube (CRT), touch screen, or any combination thereof. Outputdata from computing device 104 may be configured to be displayed on atleast an electric aircraft 152 and/or user device 148 using an outputgraphical user interface. An output graphical user interface may displayany output as described in the entirety of this disclosure. Further,authentication module 144 and/or computing device 104 may be configuredto receive a credential from instructor device 116. Instructor device116 may include any additional computing device as described above,wherein the additional computing device is utilized by and/or associatedwith a certified flight instructor. As a further embodiment,authentication module 144 and/or computing device 104 may be configuredto receive a credential from admin device 120. Admin device 120 mayinclude any additional computing device as described above in furtherdetail, wherein the additional computing device is utilizedby/associated with an employee of an administrative body, such as anemployee of the federal aviation administration.

With continued reference to FIG. in a non-limiting embodiment, thecredential may include a username and password unique to an electricaircraft. For instance, a recharging pad may attempt to authenticate anelectric aircraft to confirm it is an electric aircraft and that it iscompatible to charge the electric aircraft. In a non-limitingembodiment, authentication module 144 may transmit an aircraftperformance model output 136 to a user device when the credentials of auser and/or electric aircraft have been verified. In a non-limitingembodiment, authentication module 144 may manipulate the aircraftperformance model output 136 to be displayed to a user with varyingauthority. Authentication module 144 may incorporate priorityclassifiers used to classify low, average, and high classification ofauthorized users and/or electric aircraft. For instance, a user with alower priority classification may be a passenger or an electricaircraft, a mechanic operating maintenance on the electric aircraft, andthe like thereof. Users with lower priority classifications detected byauthentication module 144 may allow a limited amount of aircraftinformation to be displayed to a user device 148 for viewing by theusers with lower priority classification. Limited amount of aircraftinformation may include a limited aircraft performance model output 136.In another example, user with a high priority classification may be afleet manager, a captain or lead pilot of an electric aircraft, an ownerof at least an electric aircraft, and the like thereof. In anon-limiting embodiment, authentication module 144 may detect users withhigh priority classifications and transmit a robust aircraft performancemodel output 136 that may include, but not limited to, three-dimensionalflight simulation including predictive pilot and cargo movement, historyand/or record of pilot information, schedule information, and the likethereof, a robust performance alert 148, or a combination of the likethereof a person of ordinary skill in the art, after viewing theentirety of this disclosure, would appreciate the various amount ofinformation allowed to be viewed for different levels of authority.

With continued reference to FIG. 1 , in a non-limiting embodiment,authentication module 144 may be used by an electric aircraftinfrastructure. Electric aircraft infrastructure may include anyinfrastructure that may support an electric aircraft. electric aircraftinfrastructure may include at least a computing device. electricaircraft infrastructure may include, but not limited to, recharging pad,docking terminal, electric aircraft hangar, fleet hangar, and the likethereof. Electric aircraft infrastructure may include its ownauthentication module. In a non-limiting embodiment, an electricaircraft infrastructure may authenticate an electric aircraft in the airand allow the electric aircraft to land on the electric aircraftinfrastructure. In a non-limiting embodiment, an electric aircraftinfrastructure may deny or unauthorize an electric aircraft from landingon the infrastructure. For instance, the authentication module of anelectric aircraft may include identifying information that the electricaircraft is supposed to be flying, is supposed to arrive at a locationand/or electric aircraft infrastructure, is a valid electric aircraftthat is not stolen, and the like thereof. In a non-limiting embodiment,authentication module is used to verify users and/or electric aircraft.In a non-limiting embodiment, authentication module is used as asecurity measure for physical and electronic applications. A person ofordinary skill in the art, after viewing the entirety of thisdisclosure, would appreciate the function of an authentication module inthe context of secure data exchange.

With continued reference to FIG. 1 , the flight controller 120 isconfigured to send the aircraft performance model output 136 to a userdevice 148 wherein the user device is configured to receive the aircraftperformance model output 136 and display the aircraft performance modeloutput 136 by a graphical user interface (GUI). User device 148 mayreceive the performance alert and display it by a GUI. A “user device,”for the purposes of this disclosure, refer to any user device orcomputing device that a user may interact with to view and control aplurality of information. User device 148 may include a user device.User device may include any computing device, wearable computer, mobiledevice, remote device, and the like. User device 148 may include aninput device. Input device may include, but are not limited to, analpha-numeric input device (e.g., a keyboard), a pointing device, ajoystick, a gamepad, an audio input device (e.g., a microphone, a voiceresponse system, etc.), a cursor control device (e.g., a mouse), atouchpad, an optical scanner, a video capture device (e.g., a stillcamera, a video camera), a touchscreen, an inceptor stick, and anycombinations thereof. Output device 148 may receive input from userthrough standard I/O interface such as ISA (Industry StandardArchitecture), PCI (Peripheral Component Interconnect) Bus, and thelike. User device 148 may receive input from user through standard I/Ooperation. In a non-limiting embodiment, user device 148 may furtherreceive input from user through optical tracking of motion. In anon-limiting embodiment, user device 148 may further receive input fromuser through voice-commands. User device 148 may further useevent-driven programming, where event listeners are used to detect inputfrom user and trigger actions based on the input.

With continued reference to FIG. 1 , the user device 148 may include adisplay which may include a graphical user interface (GUI) 148. GUI 148may be configured to display the aircraft performance model output 136.As described herein, a GUI 148 is a form of user interface that allowsusers to interact with the controller through graphical icons and/orvisual indicators. The user may, without limitation, interact with GUI148 through direct manipulation of the graphical elements. GUI 148 maybe configured to display at least an element of a flight plan, asdescribed in detail below. As an example, and without limitation, GUI148 may be displayed on any electronic device, as described herein, suchas, without limitation, a computer, tablet, remote device, and/or anyother visual display device. User device 148 may be configured topresent, to a user, information related to the flight plan, flight planschedule, a maintenance and/or repair schedule, a flight simulation,pilot information, customer experience information, and the like. Userdevice 148 may include a multi-function display (MFD), primary display,gauges, graphs, audio cues, visual cues, information on a heads-updisplay (HUD) or a combination thereof. User device 148 may include adisplay disposed in one or more areas of an electric aircraft, on a userdevice remotely located, one or more computing devices, or a combinationthereof. Display may be disposed in a projection, hologram, or screenwithin a user's helmet, eyeglasses, contact lens, or a combinationthereof. GUI 104 may display a flight simulation in graphical form.Graphical form may include a two-dimensional plot of two variables thatrepresent data received by the controller, such as past maneuvers andpredicted future maneuvers. In one embodiment, GUI 104 may also displaythe user's input in real-time. in a non-limiting embodiment, user device148 may be used to schedule, modify, and/or cancel a maintenance/repairschedule for an electric aircraft. In a non-limiting embodiment, aflight manager may log in to a flight management control 148 via theuser device 148 and/or GUI 148 to schedule, modify, and/or cancel aplurality of schedules from the aircraft performance model output 136.For example, the flight manager may manage a fleet of electric aircraftand be informed of various operations the fleet of electric aircraft isor may be assigned. The flight management control 148 may include aconsumer tool 148 that may be configured to resolve a plurality ofconsumer related incidents. For example, a consumer, a user, a pilot, apassenger, and the like thereof, may input in issue which may include acomplaint, incident, failure, experience, and the like, in which theflight manager may view and resolve each issue through the flightmanagement control 148. This may be done remotely via a remote deviceand/or remote user device 148. In a non-limiting embodiment, a user whomay include an owner of an electric aircraft, may use the user device148 to log into the flight management control 148 program to view ahistorical record of schedules related to the user's aircraft and ahistorical record describing various information of the pilot of theaircraft. For example, the user may schedule maintenance, flights, andthe like thereof for the user's own aircraft. A person of ordinary skillin the art, after viewing the entirety of this disclosure, wouldappreciate the various functionalities the user device 148.

With continued reference to FIG. 1 , the flight management control 148may be configured to resolve a plurality of complaints including a pilotcomplaint, a customer complaint, an electric aircraft staff complaint,and the like thereof. The flight management control 148 may include aflight manager that may be an automated operator configured toautomatically audit and/or resolve the plurality of complaints. In anon-limiting embodiment, a human operator may be the flight manager andresolve/audit the plurality of complaints by logging into the userdevice 148 and manually resolving/auditing the plurality of complaints.

With continued reference to FIG. 1 , an embodiment of the flightmanagement control 148 may include distinct flight management controlsbased on the user logging into the flight management control 148. Forinstance, the flight management control 148 may provide different levelsof access and information depending on the user that may be logging intothe flight management control 148. For instance, a flight manager whomay have a higher authority in the management and operation of a fleetof aircraft may have access and power to more information, schedules,simulations, issues, and the like, whereas a user who may include apassenger or owner of an electric may have access and power to a limitedamount of information and actions. In a non-limiting embodiment, aflight manager may use the flight management control 148 to scheduleand/or conduct various commercial applications regarding an electricaircraft. In a non-limiting embodiment, a passenger of an electricaircraft may use a fleet management control 148 and log into the flightmanagement control as a passenger and may only have the authority toview logistical data of within the aircraft performance model output 136such as pilot information and flight plan schedules. In a non-limitingembodiment, an owner of an electric aircraft may have access to moreinformation including, but not limited to, historical record ofmaintenance schedules, maintenances performed on the owner's electricaircraft, a consumer tool that the owner of the electric may use toconduct commercial applications, and the like thereof.

Now referring to FIG. 2 , a block diagram of an exemplary embodiment ofa schedule database 200 is illustrated. The flight controller 120 isfurther configured to generate the aircraft performance model output 136as a function of at least a schedule database 200. A plurality ofaircraft performance model outputs 136 may be stored and/or retrieved inschedule database 200. The plurality of measured aircraft operationdatum, which may be used for generating a training data, may also bestored and/or retrieved from schedule database 200. Flight controller120 may receive, store, and/or retrieve the training data, the pluralityof aircraft performance model outputs 136, and the like, from scheduledatabase 200. Flight controller 120 may store and/or retrievemachine-learning models, classifiers, among other determinations, I/Odata, heuristics, algorithms, and the like, from schedule database 200.Schedule database 200 may be implemented, without limitation, as arelational database, a key-value retrieval database such as a NOSQLdatabase, or any other format or structure for use as a database that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure. Schedule database 200 may alternatively oradditionally be implemented using a distributed data storage protocoland/or data structure, such as a distributed hash table and the like.Schedule database 200 may include a plurality of data entries and/orrecords, as described above. Data entries in schedule database 200 maybe flagged with or linked to one or more additional elements ofinformation, which may be reflected in data entry cells and/or in linkedtables such as tables related by one or more indices in a relationaldatabase. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which data entries ina database may store, retrieve, organize, and/or reflect data and/orrecords as used herein, as well as categories and/or populations of dataconsistent with this disclosure.

With continued reference to FIG. 2 , schedule database 200 may include,without limitation, maintenance schedule table 204, flight plan scheduletable 208, flight simulation table 212, and/or health status table 206.Schedule database 200 may include a heuristic table. Determinations by amachine-learning process, machine-learning model, ranking function,and/or classifier, may also be stored and/or retrieved from the scheduledatabase 200. As a non-limiting example, schedule database 200 mayorganize data according to one or more instruction tables. One or moreschedule database 200 tables may be linked to one another by, forinstance in a non-limiting example, common column values. For instance,a common column between two tables of schedule database 200 may includean identifier of a submission, such as a form entry, textual submission,accessory device tokens, local access addresses, metrics, and the like,for instance as defined herein; as a result, a search by a flightcontroller 120 may be able to retrieve all rows from any tablepertaining to a given submission or set thereof. Other columns mayinclude any other category usable for organization or subdivision ofdata, including types of data, names and/or identifiers of individualssubmitting the data, times of submission, and the like; persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various ways in which data from one or more tables may belinked and/or related to data in one or more other tables.

Continuing in reference to FIG. 2 , in a non-limiting embodiment, one ormore tables of schedule database 200 may include, as a non-limitingexample, maintenance schedule table 204, which may include categorizedidentifying data, as described above, including a plurality of aircraftperformance model outputs 136 including a plurality of previousmaintenance schedules corresponding to an electric aircraft of aplurality of electric aircraft, potential maintenance schedules anelectric aircraft may be assigned, and the like. Maintenance scheduletable 204 may include payload data 112 categories including, but notlimited to, tests, measurements, replacements, adjustments, repairs,specific aircraft replacement parts, identification numbers denotingindividual maintenance that was performed, and the like, categories, andmay include linked tables to mathematical expressions that may describethe result and process of each maintenance or maintenance schedule. Oneor more tables may include, without limitation, a heuristic table, whichmay organize rankings, scores, models, outcomes, functions, numericalvalues, scales, arrays, matrices, and the like, which representdeterminations, probabilities, metrics, parameters, values, standards,indexes, and the like, include one or more inputs describing potentialmathematical relationships, as described herein. In a non-limitingembodiment, the flight controller 120 may retrieve a maintenanceschedule from the schedule database 200 which may be used as an inputfor the generation of the aircraft performance model output 136 andconfigured to be viewed and/or modified by a user via a user device 148.For example, a user may use a user device that includes a flightmanagement control 148 which may include a program in which a user caninteract with to view a log of maintenance, maintenance schedules, andthe like thereof. The user may further interact with the flightmanagement control to schedule, modify, and/or cancel any futuremaintenance that may be performed on an electric aircraft.

Continuing in reference to FIG. 2 , in a non-limiting embodiment, one ormore tables of schedule database 200 may include, as a non-limitingexample, a flight plan schedule table 208, which may include categorizedidentifying data, as described above, including a plurality of aircraftperformance model outputs 136 including a plurality of flight plansincluding distinct individual flight plans representing a plurality ofalternative flight plans for distinct and separate electric aircraft,and the like. Flight plan table may include flight plan categoriesaccording to aircraft destination, type of aircraft, weight of cargo ofthe aircraft, and the like, categories, and may include linked tables tomathematical expressions that describe the impact of each alternativeflight plan. In a non-limiting embodiment, the flight controller 120 mayretrieve a flight plan from the schedule database 200 to be confirmed ormodified by an ATC. In a non-limiting embodiment, the pilot and/orflight controller 120 may retrieve an alternative flight plan which mayrepresent a new flight plan or new flight plan schedule to be confirmedby the ATC authority. In a non-limiting embodiment, the flightcontroller 120 may send and receive constant transmissions of radiofrequency signals with the central authority to receive a verificationof a flight plan without a direct communication between one or morehuman operators.

With continued reference to FIG. 2 , a new flight plan or new flightplan schedule may be generated as a function of a machine-learning modelusing a training set that may include a plurality of flight plans andflight plan data from the schedule database 200 and the aircraftcomponent state data 108 from the plurality of measured aircraftoperation datum. In a non-limiting embodiment, the flight controller 120may retrieve one or more flight plan schedule from the schedule database200 which may be used as an input for the generation of the aircraftperformance model output 136 and configured to be viewed and/or modifiedby a user via a user device 148. For example, a user may use a userdevice that includes a flight management control 148 which may include aprogram in which a user can interact with to view a plurality of flightplan schedules, and the like thereof. The user may further interact withthe flight management control to schedule, modify, and/or cancel anyfuture flights for an electric aircraft.

Continuing in reference to FIG. 2 , in a non-limiting embodiment, one ormore tables of schedule database 200 may include, as a non-limitingexample, a flight simulation table 212, which may include categorizedidentifying data, as described above, including a plurality of aircraftperformance model outputs 136 including a plurality of flightsimulations. Flight simulation table may include three dimensionalmodeling of a flight, a two dimensional modeling of a flight, and thelike, and may include linked tables to mathematical expressions thatdescribe the impact of each flight simulation. Flight simulation tablemay include simulation of flight mimicking a plurality of incidencesthat may occur during a flight. Incidences may include an electricaircraft malfunction, a pilot or passenger complaint, an issuecorresponding to a delivery, shipment, or cargo of an electric aircraft,and the like thereof. In a non-limiting embodiment, the flightcontroller 120 may retrieve one or more flight simulations from theschedule database 200 which may be used as an input for the generationof the aircraft performance model output 136 and configured to be viewedand/or modified by a user via a user device 148. For example, a user mayuse a user device that includes a flight management control 148 whichmay include a program in which a user can interact with to view aplurality flight simulations, and the like thereof. The user may furtherinteract with the flight management control to view the flightsimulations, assess and/or analyze a flight as a function of a flightsimulation, and the like thereof. For example, if an incident hasoccurred during a flight, a user may receive and view a flightsimulation replicating the flight and incident to better understand theincident that occurred and resolve any issue or complaint from a pilotand/or passenger.

Further referring to FIG. 2 , schedule database 200 may include, withoutlimitation, a heuristic table. Determinations by a machine-learningprocess, machine-learning model, ranking function, and/or classifier,may also be stored and/or retrieved from the schedule database 200. As anon-limiting example, schedule database 200 may organize data accordingto one or more instruction tables. One or more schedule database 200tables may be linked to one another by, for instance in a non-limitingexample, common column values. For instance, a common column between twotables of schedule database 200 may include an identifier of asubmission, such as a form entry, textual submission, accessory devicetokens, local access addresses, metrics, and the like, for instance asdefined herein; as a result, a search by the flight controller 120 maybe able to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of data, including types of data,names and/or identifiers of individuals submitting the data, times ofsubmission, and the like; persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in which datafrom one or more tables may be linked and/or related to data in one ormore other tables. In a non-limiting embodiment, flight plans may begenerated manually by a pilot in any moment and the flight plans may bestored in the database 200 or transmitted from a separate entity such asa remote device.

Referring now to FIG. 3 , a block diagram of an exemplary embodiment ofan aircraft performance model output 300 is illustrated. The aircraftperformance model output 304 includes a maintenance schedule 308. Themaintenance schedule may include any maintenance schedule describedherein. The maintenance schedule 308 may include a repair schedule. In anon-limiting embodiment, the aircraft performance model output 304 mayinclude a plurality of records of a plurality of maintenance schedulesfor a plurality of electric aircraft of a fleet of electric aircraft. Ina non-limiting embodiment, the maintenance schedule 308 may be affectedby, in part, a user device 148, flight controller 120, and the likethereof. In a non-limiting embodiment, the maintenance scheduleincluding a repair schedule may be generated as a function of a loweredhealth status of an electric component of an electric aircraft from atleast the aircraft health datum.

Still referring to FIG. 3 , the aircraft performance model output 304may include a flight plan schedule 312. The flight plan schedule may bedetermined by a central authority via a digital communication including,but not limited to, the transmission of a plurality of signals and/orradio frequency signals. A “central authority,” for the purposes of thisdisclosure, refer to an authorizing entity such as an air trafficcontrol (ATC) operator. The central authority may determine a flightplan 320 that an electric aircraft may schedule. In a non-limitingembodiment, the flight plan schedule 312 may be the flight plan 320 thatis selected by the central authority 316. In a non-limiting embodiment,the flight plan schedule 312 may include a plurality of flight planschedules that a flight manager may select one as the designated flightplan schedule for an electric aircraft. A flight manager may communicatewith the central authority 316 via a network as a function of the flightcontroller 120 or the aircraft performance model output 304. In anon-limiting embodiment, a flight manager may communicate directly withthe central authority via digital communication. A “digitalcommunication,” for the purposes of this disclosure, refer to a mode oftransfer and reception of data over a communication channel via digitalsignals. Digital signals may include, but not limited to, audio signals,electrical signals, video signals, radar signals, radio signals, sonarsignals, transmission signals, and the like thereof. In a non-limitingembodiment, the flight controller may include a physical may include aplurality of physical controller area network buses communicativelyconnected to the aircraft and the sensor 104. A “physical controllerarea network bus,” as used in this disclosure, is vehicle bus unitincluding a central processing unit (CPU), a CAN controller, and atransceiver designed to allow devices to communicate with each other'sapplications without the need of a host computer which is locatedphysically at the aircraft. Physical controller area network (CAN) busunit may include physical circuit elements that may use, for instanceand without limitation, twisted pair, digital circuit elements/FGPA,microcontroller, or the like to perform, without limitation, processingand/or signal transmission processes and/or tasks. For instance andwithout limitation, CAN bus unit may be consistent with disclosure ofCAN bus unit in U.S. patent application Ser. No. 17/218,342 and titled“METHOD AND SYSTEM FOR VIRTUALIZING A PLURALITY OF CONTROLLER AREANETWORK BUS UNITS COMMUNICATIVELY CONNECTED TO AN AIRCRAFT,” which isincorporated herein by reference in its entirety. In a non-limitingembodiment, the flight controller 120 may receive the plurality ofmeasured data from the sensor 104 by a physical CAN bus unit and/ortransmit a proposed flight plan to a second physical CAN bus unit of aflight management module 132 which may be configured to send and receivea plurality of signals from an air traffic control operator 152. In anon-limiting embodiment, the sensor 104 may include a physical CAN busunit to detect the plurality of measured data in tandem with a pluralityof individual sensors from a sensor suite. Physical CAN bus unit mayinclude multiplex electrical wiring for transmission of multiplexedsignaling. Physical CAN bus unit 104 may include message-basedprotocol(s), wherein the invoking program sends a message to a processand relies on that process and its supporting infrastructure to thenselect and run appropriate programing.

With continued reference to FIG. 3 , aircraft performance model output304 may include a health datum 336. A “health datum,” for the purpose ofthis disclosure, is a plurality of elements of data representing past,current, and/or predictive health statuses of a plurality of flightcomponents of an electric aircraft. health datum 336 may include aprojection or prediction of the health statuses of flight componentsafter a scheduled or projected flight. Health datum 336 may include ahistory and/or record of health statuses of the plurality of flightcomponents.

With continued reference to FIG. 3 , aircraft performance model output304 includes a flight simulation 324. A “flight simulation,” for thepurposes of this disclosure, refer to an artificially created model of aflight of an electric aircraft. In a non-limiting embodiment, a flightsimulation may include graphical lines and images representing theelectric aircraft and the path and distance an electric aircraft hastraversed or is projected to traverse. In a non-limiting embodiment, theflight simulation may include a three-dimensional (3D) and atwo-dimensional (2D) graphical representation of the flight of anelectric aircraft. For example, the flight simulation 324 may include aplurality of flight simulations that correspond to a flight planschedule 312 of a plurality of flight plan schedules determined by thecentral authority 316. Flight simulation 324 may include a flight issue328. A “flight issue,” for the purposes of this disclosure, refer to amoment that represents an incident or occurrence of an issue in theflight of an aircraft. Flight issue 328 may include an aircraft failure,at least a degradation of an aircraft component, and an avionics glitchand the like thereof. Flight issue may include any issue as described inthe entirety of this disclosure. In a non-limiting embodiment, theflight simulation 324 may generate a visual model that may include anyshape, color, size, and the like thereof, denoting when and where theflight issue may have occurred in the duration of a flight as a functionof the flight simulation 324. In a non-limiting embodiment, the flightsimulation 324 may be configured to generate a simulation of the partiesinvolved that may have caused the flight issue that may include asimulation of passengers arguing with each other, a passenger arguingwith a pilot, and the like thereof. In a non-limiting embodiment, flightsimulation 324 may replicate and/or replay the flight of an electricaircraft in a graphical form. The replay of the flight may be generatedas a function of the flight plan 320 authorized by the central authority316.

With continued reference to FIG. 3 , flight simulation 324 may include aflight issue 328. Flight issue may include any issue described in theentirety of this disclosure. In a non-limiting embodiment, a sensor 104may detect a malfunction or abnormality of an aircraft component at anytime during the lift-off, flight, and landing of an electric aircraft,pinpoint the time and location of the occurrence of the malfunction orabnormality, and transmit it to the flight simulation 324 in which theflight controller 120 may generate a flight simulation configured tosimulate the moment of the malfunction or abnormality. In a non-limitingembodiment, aircraft performance model output 304 may retrieve a flightsimulation that closely resembles the detected flight issue 328 from aflight simulation table from the schedule database. The simulation ofthe flight issue 328 moment may be used by a flight manager to resolve,analyze, view, and the like thereof, the flight issue 328. Flightsimulation 324 may include a symbol indicating a flight plan 320 of theelectric aircraft as a form of a flight plan schedule 312. The symbolmay include any shape, color, size, indicator, and the like thereof.Flight simulation 324 may include a warning sign indicating an issueconcerning the flight plan. The warning sign may include any indicatorthat is configured to capture the attention of any user. A warning signmay be communicatively connected to a designed siren or sound configuredto capture the attention of a user.

With continued reference to FIG. 3 , flight simulation 324 may include acustomer complaint 332. A “customer complaint,” for the purposes of thisdisclosure, refer to an issue related to an unfavorable experience by acustomer. In a non-limiting embodiment, a customer complaint 332 may bea flight issue 328 which may further be replicated or simulated as afunction of the flight simulation 324. Complaints may include anaircraft personnel complaint which may include issues and/or complaintsfrom the personnel of the aircraft such as the pilot, flight attendants,engineers, and the like. A person of ordinary skill in the art, afterviewing the entirety of this application, would appreciate the differenttypes of flight issues and complaints that may affect the quality of aflight.

Referring now to FIG. 4 , an embodiment of authentication module 144, aspictured in FIG. 1 , is illustrated in detail. Authentication module 144may include any suitable hardware and/or software module. Authenticationmodule 144 and/or flight controller 420 can be configured toauthenticate user device 148. Authenticating, for example and withoutlimitation, can include determining a user's ability/authorization toaccess information included in each module and/or engine of theplurality of modules and/or engines operating on flight controller 420.As a further example and without limitation, authentication may includedetermining an instructor's authorization/ability of access to theinformation included in each module and/or engine of the plurality ofmodules and/or engines operating on flight controller 420. As a furthernon-limiting example, authentication may include determining anadministrator's authorization/ability to access the information includedin each module and/or engine of the plurality of modules and/or enginesoperating on flight controller 420. Authentication may enable access toan individual module and/or engine, a combination of modules and/orengines, and/or all the modules and/or engines operating on flightcontroller 420. Authenticating user device 148 is configured to receivecredential 400 from user device 148. Credential 400 may include anycredential as described above in further detail in reference to FIG. 1 .For example and without limitation, credential 400 may include ausername and password unique to the user and/or user device 148. As afurther example and without limitation, credential 400 may include a PKIcertificate unique to the user and/or user device 148. As a furtherembodiment, credential 400 may be received from instructor device 416and/or admin device 420, such that credential 400 would authenticateeach instructor device 416 and admin device 420, respectively.

Continuing to refer to FIG. 4 , authentication module 144 and/or flightcontroller 420 may be further designed and configured to comparecredential 400 from user device 148 to an authorized credential storedin authentication database 404. For example, authentication module 144and/or flight controller 420 may be configured to compare credential 400from user device 148 to a stored authorized credential to determine ifcredential 400 matches the stored authorized credential. As a furtherembodiment, authentication module 144 and/or computing device maycompare credential 400 from an instructor device 416 to an authorizedcredential stored in authentication database 404. Instructor device 416may include any additional computing device as described above, whereinthe additional computing device is utilized by and/or associated with acertified flight instructor. As a further embodiment, authenticationmodule 144 and/or flight controller 120 may be configured to receive acredential from admin device 420. Admin device 420 may include anyadditional computing device as described above in further detail,wherein the additional computing device is utilized by/associated withan employee of an administrative body, such as an employee of thefederal aviation administration. For example, authentication module 144and/or computing device may be configured to compare credential 400 frominstructor device 416 to a stored authorized credential to determine ifcredential 400 matches the stored authorized credential. As a furthernon-limiting example, authentication module 144 and/or computing devicemay match credential 400 from admin device 420 to an authorizedcredential stored in authentication database 404. For example,authentication module 144 and/or computing device may be configured tocompare credential 400 from admin device 420 to a stored authorizedcredential to determine if credential 400 matches the stored authorizedcredential. In embodiments, comparing credential 400 to an authorizedcredential stored in authentication database 404 can include identifyingan authorized credential stored in authentication database 404 bymatching credential 400 to at least one authorized credential stored inauthentication database 404. Authentication module 144 and/or computingdevice may include or communicate with authentication database 404.Authentication database 404 may be implemented as any database and/ordatastore suitable for use as authentication database 404 as describedin the entirety of this disclosure. An exemplary embodiment ofauthentication database 404 is included below in reference to FIG. 3 .The “authorized credential” as described in the entirety of thisdisclosure, is the unique identifier that will successfully authorizeeach user and/or user device 148 if received. For example and withoutlimitation, the authorized credential is the correct alpha-numericspelling, letter case, and special characters of the username andpassword for user device 148. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various examples ofauthorized credentials that may be stored in the authentication databaseconsistently with this disclosure.

Still referring to FIG. 4 , authentication module 144 and/or flightcontroller 420 is further designed and configured to bypassauthentication for user device 148 based on the identification of theauthorized credential stored within authentication database 404.Bypassing authentication may include permitting access to user device148 to access the information included in each module and/or engine ofthe plurality of modules and/or engines operating on flight controller420. Bypassing authentication may enable access to an individual moduleand/or engine, a combination of modules and/or engines, and/or all themodules and/or engines operating on flight controller 420, as describedin further detail in the entirety of this disclosure. As a furtherexample and without limitation, bypassing authentication may includebypassing authentication for instructor device 416 based on thecomparison of the authorized credential stored in authenticationdatabase 404. As a further non-limiting example, bypassingauthentication may include bypassing authentication for admin device 420based on the comparison of the authorized credential stored inauthentication database 112.

With continued reference to FIG. 4 , authentication module 144 and/orflight controller 420 may be further configured to biometricallyauthenticate user device 148. Biometric authentication, for example andwithout limitation, determines a user's ability to access theinformation included in each module and/or engine of the plurality ofmodules and/or engines operating on flight controller 420 as a functionof a biometric credential 408. Biometric authentication, in theembodiment, includes receiving biometric credential 408 from user device148, comparing and/or matching biometric credential 408 from user device148 to an authorized biometric credential stored in a biometric database412, and bypassing authentication for user device 148 based on thecomparison of the authorized biometric credential stored withinbiometric database 412. Biometric authentication employingauthentication module 144 may also include biometrically authenticatinginstructor device 416 and/or admin device 420. Authentication module 144and/or flight controller 420 may include or communicate with biometricdatabase 412. Biometric database 412 may be implemented as any databaseand/or datastore suitable for use as a biometric database entirely withthis disclosure. An exemplary embodiment of biometric database 412 isprovided below in reference to FIG. 4 . The “biometric credential” asused in this disclosure, is any body measurement and/or calculationutilized for identification purposes, such as a physiologicalcharacteristic and/or behavioral characteristic. For example and withoutlimitation, biometric credential 408 may include fingerprints, palmveins, face recognition, DNA, palm print, hand geometry, irisrecognition, retina, odor/scent, typing rhythm, gait, voice, and thelike. The “authorized biometric credential” as described in the entiretyof this disclosure, is unique biometric identifier that willsuccessfully authorize each user and/or user device 148, such that theauthorized biometric credential is the correct biometric credentialwhich will enable the user and/or user device 148 access to theplurality of modules and/or engines operating on flight controller 120.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various examples of biometric credentialsand authorized biometric credentials that may be utilized byauthentication module 144 consistently with this disclosure.

Referring now to FIG. 5 , an embodiment of authentication database 404is illustrated. Authentication database 404 may include any datastructure for ordered storage and retrieval of data, which may beimplemented as a hardware or software module. Authentication database404 may be implemented, without limitation, as a relational database, akey-value retrieval datastore such as a NOSQL database, or any otherformat or structure for use as a datastore that a person skilled in theart would recognize as suitable upon review of the entirety of thisdisclosure. Authorization database 404 may include a plurality of dataentries and/or records corresponding to credentials as described above.Data entries and/or records may describe, without limitation, dataconcerning authorized credential datum and failed credential datum.

With continued reference to FIG. 5 , one or more database tables inauthentication database 404 may include as a non-limiting example anauthorized credential datum table 500. Authorized credential datum table500 may be a table storing authorized credentials, wherein theauthorized credentials may be for user device 148, instructor device 416and/or admin device 420, as described in further detail in the entiretyof this disclosure. For instance, and without limitation, authenticationdatabase 404 may include an authorized credential datum table 500listing unique identifiers stored for user device 148, wherein theauthorized credential is compared/matched to a credential 200 receivedfrom user device 148.

Still referring to FIG. 5 , one or more database tables inauthentication database 404 may include, as a non-limiting example,failed credential datum table 504. A “failed credential,” as describedin the entirety of this disclosure, is a credential received from adevice that did not match an authorized credential stored withinauthorized credential datum table 500 of authentication database 404.Such credentials can be received from user device 148, instructor device416 and/or admin device 420. Failed credential datum table 504 may be atable storing and/or matching failed credentials. For instance andwithout limitation, authentication database 404 may include failedcredential datum table 504 listing incorrect unique identifiers receivedby a device in authentication module 144, wherein authentication of thedevice did not result. Tables presented above are presented forexemplary purposes only; persons skilled in the art will be aware ofvarious ways in which data may be organized in authentication database404 consistently with this disclosure.

Referring now to FIG. 6 , an embodiment of biometric database 412 isillustrated. Biometric database 412 may include any data structure forordered storage and retrieval of data, which may be implemented as ahardware or software module. Biometric database 412 may be implemented,without limitation, as a relational database, a key-value retrievaldatastore such as a NOSQL database, or any other format or structure foruse as a datastore that a person skilled in the art would recognize assuitable upon review of the entirety of this disclosure. Biometricdatabase 412 may include a plurality of data entries and/or recordscorresponding to elements of biometric datum as described above. Dataentries and/or records may describe, without limitation, data concerningparticular physiological characteristics and/or behavioralcharacteristics that have been collected. Data entries in a biometricdatabase 412 may be flagged with or linked to one or more additionalelements of information, which may be reflected in data entry cellsand/or in linked tables such as tables related by one or more indices ina relational database; one or more additional elements of informationmay include data associating a biometric with one or more cohorts,including demographic groupings such as ethnicity, sex, age, income,geographical region, or the like. Additional elements of information mayinclude one or more categories of biometric datum as described above.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which data entries in abiometric database 412 may reflect categories, cohorts, and/orpopulations of data consistently with this disclosure.

Still referring to FIG. 6 , one or more database tables in biometricdatabase 412 may include, as a non-limiting example, fingerprint datatable 600. Fingerprint data table 600 may be a table correlating,relating, and/or matching biometric credentials received from a device,such as user device 148, instructor device 416 and admin device 420, asdescribed above, to fingerprint data. For instance, and withoutlimitation, biometric database 412 may include a fingerprint data table600 listing samples acquired from a user having allowed system 100 toretrieve fingerprint data from user device 148 through a fingerprintscanner, including biometric scanners such as optical scanners orcapacitive scanners, one or more rows recording such an entry may beinserted in fingerprint data table 600.

With continued reference to FIG. 6 , biometric database 412 may includetables listing one or more samples according to a sample source. Forinstance, and without limitation, biometric database 412 may includetyping rhythm database 604 listing samples acquired from a user byobtaining the user's keystroke dynamics when typing characters on akeyboard and/or keypad, such as the time to get to and depress a key,duration the key is held down, use of caps-lock, pace of typingcharacters, misspellings, or the like. As another non-limiting example,biometric database 412 may include face recognition data table 608,which may list samples acquired from a user associated with user device148 that has allowed system 100 to obtain digital images or video framesof the user's facial demographics, such as relative position, size,and/or shape of the eyes, nose, cheekbones, jaw, and/or the like. As afurther non-limiting example, biometric database 412 may include a voicerecognition data table 612, which may list samples acquired from a userassociated with user device 148 that has allowed system 100 to retrievethe user's unique voice patterns though a microphone located on userdevice 148, such as dictation variants, common phrases, volume level,dialect, pitch, format frequencies, and/or the like. As a furtherexample, also non-limiting, biometric database 412 may include iris scandata table 616, which may list samples acquired from a user associatedwith user device 148 that has allowed system 100 to retrieve a user'siris scan from a camera located on user device 148, including withoutlimitation images of the detailed structures of the iris which arevisible externally. As another non-limiting example, biometric database412 may include retinal scan data table 620, which may include samplesacquired from a user associated with user device 148 that has allowedsystem 100 to extract a user's retinal scan; retinal scans may includean image of the complex and unique structure of an individual'scapillaries in the retina. Tables presented above are presented forexemplary purposes only; persons skilled in the art will be aware ofvarious ways in which data may be organized in biometric database 412consistently with this disclosure.

Referring now to FIG. 7 , an embodiment of flight management engine 700is illustration. Flight management engine 700 may be included in aflight controller 120. Flight management engine 700 can be designed andconfigured to include registry module 704 and scheduler module 712operating thereon. Registry module 704 and scheduler module 712 mayinclude any suitable software and/or hardware as described in theentirety of this disclosure. In an embodiment, registry module 704and/or flight controller 120 can be designed and configured to receive aplurality of maneuver data from instructor device 416 and record eachmaneuver data of the plurality of maneuver data in a pilot log database.Further, scheduler module 712 and/or flight controller 120 may beconfigured to generate available flight determinations as a function ofan airport metrics datum received from an ATC central authority 316 andpilot restriction received from user device 148 and/or sensor 104.Flight management engine 700 may include any suitable software and/orhardware module as described in the entirety of this disclosure. In anembodiment, flight management engine 700 can be configured to includeregistry module 704. Registry module 704 and scheduler module 712 mayinclude any suitable software and/or hardware as described in theentirety of this disclosure. In an embodiment, registry module 704and/or flight controller 120 can be designed and configured to receive aplurality of maneuver data from instructor device 416 and record eachmaneuver data of the plurality of maneuver data in a pilot log database.Registry module 704 and/or flight controller 120 may include anysuitable hardware and/or software module as described in the entirety ofthis disclosure. Registry module 704 operating on flight managementengine 700 and/or flight controller 120 may be configured to receive aplurality of maneuver data from instructor device 416. The “plurality ofmaneuver data” as described in the entirety of this disclosure is datadescribing completion by the pilot of procedures and concepts thatcontrol the electric aircraft. The electric aircraft, for example andwithout limitation, may include flight simulator 128 and/or electricaircraft 152. Flight simulator 128 may include any flight simulator asdescribed in the entirety of this disclosure. Electric aircraft 152 mayinclude any electric aircraft as described in the entirety of thisdisclosure. For example and without limitation, the plurality ofmaneuver data may include foundational flight maneuvers, such asstraight-and-level turns, climbs and descents, and/or performancemaneuvers, such that the application of flight control pressures,attitudes, airspeeds, and orientations are constantly changingthroughout the maneuver. For example and without limitation, theplurality of maneuver data may include, ground reference maneuvers, suchas turns around a point, s-turns, rectangular ground maneuvering course,eights along A road, eights around pylons, hover taxi, air taxi, surfacetaxi, and the like. As a further example and without limitation, theplurality of maneuver data may include takeoffs and landings, such asnormal takeoff and climb, crosswind takeoff and climb, short fieldtakeoff and climb, normal takeoff from a hover, vertical takeoff to ahover, short field approach and landing, soft field approach andlanding, touch and go, power-off 180 approach and landing, normalapproach to a hover, crosswind approach to the surface, and the like.The plurality of maneuver data may further include, for example andwithout limitation, airborne maneuvers, such as trimming the aircraft,slow flight, lazy eights, chandelle, straight and level flight, turns,steep turns, unusual attitudes, spatial disorientation demonstration,hovering, hovering turn, rapid deceleration, reconnaissance procedures,and the like. The plurality of maneuver data, as a further non-limitingexample, may include emergency preparedness, such as steep spirals,emergency approach and landing, spins, ditching, autorotation, vortexring state, retreating blade stall, ground resonance, dynamic rollover,low rotor RPM, systems malfunction, flight diversions, and the like.Further, the plurality of maneuver data may include, as a non-limitingexample, instrument procedures, such as aircraft holding procedures,arcing approach, instrument landing system approach, instrumentreference climbs and descents, basic attitude instrument flight, and thelike. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various procedures and concepts that mayrepresent the plurality of maneuver data consistently with thisdisclosure.

Still referring to FIG. 7 , registry module 704 operating on flightmanagement engine 700 and/or flight controller 120 may be furtherconfigured to record each maneuver data of the plurality of maneuverdata in a pilot log database 716. Flight management engine 700, registrymodule 704 and/or flight controller 120 may include or communicate withpilot log database 716. Pilot log database 716 may be implemented as anydatabase and/or datastore suitable for use as pilot log database 716 asdescribed in the entirety of this disclosure. Each maneuver data of theplurality of maneuver data may be recorded in any suitable data and/ordata type. For instance, and without limitation, each maneuver data mayinclude textual data, such as numerical, character, and/or string data.Textual data may include a standardized name and/or requirement for aprocedure, technique, maneuver, or the like; requirements may includeregulatory requirements to meet certification standards, which mayinclude without limitation certification standards utilized byadministrative bodies such as The Federal Aviation Administration (FAA).In general, there is no limitation on forms textual data or non-textualdata the plurality of maneuver data may take; persons skilled in theart, upon reviewing the entirety of this disclosure, will be aware ofvarious forms which may be suitable for use as the plurality of maneuverdata consistently with this disclosure.

With continued reference to FIG. 7 , registry module 704 operating onflight management engine 700 and/or flight controller 120 may be furtherconfigured to receive a plurality of tactile movement data from a sensor704, wherein sensor 704 is disposed on electric aircraft 152, andgenerate a maneuver model output including each maneuver data of theplurality of maneuver data utilizing a selected training set 708. The“sensor” as described herein is a component capable of detecting themotion of the electric aircraft in multiple dimensions. For example andwithout limitation, sensor 704 may include a gyroscope, accelerometer,rate sensor, microwave systems, ultrasonic systems. Sensor 704 may beconfigured to be mechanically and/or electronically coupled to electricaircraft 152 internally and/or externally. Sensor may further include asensor integrated into any instrumentation of electric aircraft 152,wherein sensor 704 would detect motion of the instrument in multipledimensions. In a further embodiment, sensor 704 may be configured to bedisposed on flight simulator 128. Flight management engine 700, registrymodule 704 and/or flight controller 120 may be configured to receive theplurality of tactile movement data from flight simulator 128. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various sensors and motion detectors that may representsensor 704 consistently with this disclosure.

Still referring to FIG. 7 , flight controller 120, flight managementengine 700, registry module 704, flight controller 120 and/or one ormore modules operating thereon may be designed and/or configured toperform any method, method step, or sequence of method steps in anyembodiment described in this disclosure, in any order and with anydegree of repetition. For instance, flight controller 120, flightmanagement engine 700 and/or registry module 704 may be configured toperform a single step or sequence repeatedly until a desired orcommanded outcome is achieved; repetition of a step or a sequence ofsteps may be performed iteratively and/or recursively using outputs ofprevious repetitions as inputs to subsequent repetitions, aggregatinginputs and/or outputs of repetitions to produce an aggregate result,reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Flight controller 120,flight management engine 700 and/or registry module 704 may perform anystep or sequence of steps as described in this disclosure in parallel,such as simultaneously and/or substantially simultaneously performing astep two or more times using two or more parallel threads, processorcores, or the like; division of tasks between parallel threads and/orprocesses may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Continuing to refer to FIG. 7 , flight controller 120, flight managementengine 700 and/or registry module 704 may be designed and configured toreceive training data. Training data, as used herein, is data containingcorrelations between two or more sub-sets of data that amachine-learning process may use to model relationships between two ormore categories of data elements. For instance, and without limitation,training data may include a plurality of data entries, each entryrepresenting a set of data elements that were recorded, received, and/orgenerated together; data elements may be correlated by shared existencein a given data entry, by proximity in a given data entry, or the like.Multiple data entries in training data may evince one or more trends incorrelations between categories of data elements; for instance, andwithout limitation, a higher value of a first data element belonging toa first category of data element may tend to correlate to a higher valueof a second data element belonging to a second category of data element,indicating a possible proportional or other mathematical relationshiplinking values belonging to the two categories. Multiple categories ofdata elements may be related in training data according to variouscorrelations; correlations may indicate causative and/or predictivelinks between categories of data elements, which may be modeled asrelationships such as mathematical relationships by machine-learningprocesses as described in further detail below. Training data may beformatted and/or organized by categories of data elements, for instanceby associating data elements with one or more descriptors correspondingto categories of data elements. As a non-limiting example, training datamay include data entered in standardized forms by persons or processes,such that entry of a given data element in a given field in a form maybe mapped to one or more descriptors of categories. Elements in trainingdata may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training datamay be provided in fixed-length formats, formats linking positions ofdata to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 7 , trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance, natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grams containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a tactical movement and/or a flight procedure performed by theelectric aircraft may be identified by reference to a list, dictionary,or other compendium of terms, permitting ad-hoc categorization bymachine-learning algorithms, and/or automated association of data in thedata entry with descriptors or into a given format. The ability tocategorize data entries automatedly may enable the same training data tobe made applicable for two or more distinct machine-learning algorithmsas described in further detail below.

With continued reference to FIG. 7 , registry module 704 operating onflight management engine 700 and/or flight controller 120 may beconfigured to select a training set 708 including a plurality ofmaneuver data and a correlated element of flight movement data, asdescribed in further detail below in reference to FIG. 8 . The“plurality of maneuver data” as described in the entirety of thisdisclosure is data describing completion by the pilot of procedures andconcepts that control an electric aircraft. the plurality of maneuverdata may include foundational flight maneuvers, such asstraight-and-level turns, climbs and descents, and/or performancemaneuvers, such that the application of flight control pressures,attitudes, airspeeds, and orientations are constantly changingthroughout the maneuver. For example and without limitation, theplurality of maneuver data may include, ground reference maneuvers, suchas turns around a point, s-turns, rectangular ground maneuvering course,eights along A road, eights around pylons, hover taxi, air taxi, surfacetaxi, and the like. As a further example and without limitation, theplurality of maneuver data may include takeoffs and landings, such asnormal takeoff and climb, crosswind takeoff and climb, short fieldtakeoff and climb, normal takeoff from a hover, vertical takeoff to ahover, short field approach and landing, soft field approach andlanding, touch and go, power-off 180 approach and landing, normalapproach to a hover, crosswind approach to the surface, and the like.The plurality of maneuver data may further include, for example andwithout limitation, airborne maneuvers, such as trimming the aircraft,slow flight, lazy eights, chandelle, straight and level flight, turns,steep turns, unusual attitudes, spatial disorientation demonstration,hovering, hovering turn, rapid deceleration, reconnaissance procedures,and the like. The plurality of maneuver data, as a further non-limitingexample, may include emergency preparedness, such as steep spirals,emergency approach and landing, spins, ditching, autorotation, vortexring state, retreating blade stall, ground resonance, dynamic rollover,low rotor RPM, systems malfunction, flight diversions, and the like.Further, the plurality of maneuver data may include, as a non-limitingexample, instrument procedures, such as aircraft holding procedures,arcing approach, instrument landing system approach, instrumentreference climbs and descents, basic attitude instrument flight, and thelike. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various procedures and concepts that mayrepresent the plurality of maneuver data consistently with thisdisclosure. For example and without limitation, the plurality ofmaneuver data may include, ground reference maneuvers, takeoffs andlandings, airborne maneuvers, emergency preparedness, instrumentprocedures, and the like, and/or any combination thereof, as describedin further detail in the entirety of this disclosure. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various procedures and concepts that may represent the plurality ofmaneuver data consistently with this disclosure.

Still referring to FIG. 7 , the element of flight movement data mayinclude any data indicative of a pilot's maneuver performed and/orattempted to perform; maneuvers may be evaluated with regard to one ormore measures of flight control, one or more measures of navigationalcontrol, one or more measures of instrumentation, one or more measuresof aircraft positioning, one or more measures of maneuver accuracy,and/or any other subdivision of an aircraft useful for training,descriptive, or analytic purposes. The element of flight movement datamay include, without limitation, takeoff data, such as runway alignment,threshold of takeoff pitch attitude, wherein there is a proper range ofangle formed between the airplane's longitudinal axis during takeoff,threshold of takeoff bank attitude, wherein there is a proper range ofangle formed between the airplane's lateral axis, threshold of takeoffheading of the aircraft, wherein there is a proper range of directionthe nose of electric aircraft 152 and/or flight simulator 128 is pointedin during takeoff, threshold of takeoff airspeed, wherein there is aproper range of airspeed required to perform a successful takeoff,threshold of rotor speed for vertical takeoff, wherein there is a properrange of speed of rotation of the rotors of electric aircraft 152 and/orflight simulator 128 during takeoff, and the like.

Still referring to FIG. 7 , the element of flight movement data mayinclude, without limitation, landing data, such as runway alignment,threshold angle of vertical descent, wherein there is a proper range ofdegree of the angle formed between the electric aircraft 152 and/orflight simulator 128 and the ground during vertical landing, thresholdof angle of landing, wherein there is a proper range of the measurementof the angle formed between electric aircraft 152 and/or flightsimulator 128 and the ground during fixed-wing landing, threshold oflanding pitch attitude, wherein there is a proper range of angle formedbetween the airplane's longitudinal axis during landing, threshold oflanding bank attitude, wherein there is a proper range of angle formedbetween the airplane's lateral axis during landing, threshold of headingof the aircraft during landing, wherein there is a proper range ofdirectional movement of the nose of electric aircraft 152 and/or flightsimulator 128 during landing, threshold of landing airspeed, whereinthere is a proper range of airspeed required to perform a successfullanding, threshold of vibrational frequency during landing, threshold ofrotor speed for vertical landing, wherein there is a proper speed ofrotation of the rotors of electric aircraft 152 and/or flight simulator128 during landing, and the like.

Continuing to refer to FIG. 7 , the element of flight movement data mayfurther include, without limitation, turn data, such as threshold ofbank angle for a shallow turn, wherein the bank angle of electricaircraft 152 and/or flight simulator 128 is less than 20 degrees,threshold of bank angle for a medium turn, wherein the bank angle ofelectric aircraft 152 is between 20 and 45 degrees, threshold of bankangle for steep turns, wherein the bank angle of electric aircraft 152and/or flight simulator 128 is greater than 45 degrees, threshold of yawin the direction of the turn, wherein there is a proper range of yaw foreach turn of the plurality of turns of an aircraft, threshold of yaw inthe direction opposite the turn, wherein there is a proper range of yawfor the opposite direction of each turn of the plurality of turns of theaircraft, threshold of airspeed during turn, wherein there is a range ofallowable airspeed for turning, threshold of heading of the aircraftduring turns, wherein there is a proper range of directional movement ofthe nose of electric aircraft 152 and/or flight simulator 128 duringturning, and the like.

Still referring to FIG. 7 , the element of flight movement data mayfurther include, without limitation, instrumentation data, such as athreshold of vertical speed, wherein there is a proper range of speed tobe maintained and/or reached during vertical flight, threshold ofattitude, wherein there is a proper range of orientation of the aircraftrelative to the earth to be reached and/or maintained during verticaland/or horizontal flight, threshold of altimeter, wherein there is aproper range of altitude electric aircraft 152 and/or flight simulator128 must meet and/or maintain during vertical flight, horizontal flight,a turn, a stall, a descent, a roll, a loop, and the like, threshold ofairspeed of horizontal flight, wherein there is a proper range of speedto be maintained and/or reached during horizontal flight, and the like.

With continued reference to FIG. 7 , the element of flight movement datamay include, without limitation, emergency protocol data, such as athreshold of time to recovery of a system malfunction and/or failure,such as power failure in a rotor, power failure in a propeller, damageto a wing, damage to the fuselage, dynamic rollover, damage to a rotor,malfunction with the collective, malfunction with the inceptor stick,and the like, threshold attitude during a malfunction, wherein there isa proper range of attitude to reach to recover from the malfunctionand/or emergency scenario, such as a dynamic rollover, running takeoff,steep approach to a hover, shallow approach landing, and the like,threshold level of hover, wherein there is a proper range of hover to bemaintained and/or reached to recover from an emergency scenario, such asvortex ring state, tailspin, and the like. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousdata indicative of a flight maneuver that may represent the element offlight movement data consistently with this disclosure.

Continuing to refer to FIG. 7 , the element of flight movement data maybe stored in any suitable data and/or data type. In an embodiment, theelement of flight movement data may be stored in maneuver database 720.Flight management engine 700 and/or registry module 704 and/or flightcontroller 120 may include or communicate with maneuver database 720.Maneuver database 720 may be implemented as any database and/ordatastore suitable for use as maneuver database 720 as described in theentirety of this disclosure. For instance, and without limitation, theelement of flight movement data may include textual data, such asnumerical, character, and/or string data. In general, there is nolimitation on forms textual data or non-textual data the element offlight movement data may take; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousforms which may be suitable for use as at least an element of flightmovement data consistently with this disclosure.

Still referring to FIG. 7 , flight management engine 700 may be furtherconfigured to include scheduler module 712. Scheduler module 712 mayinclude any suitable hardware and/or software module as described in theentirety of this disclosure. In an embodiment, scheduler module 712and/or flight controller 120 may be configured to receive an airportmetrics datum from an ATC central authority 316 wherein the central ATCcentral authority may include any central authority as described herein.The “airport metrics datum” as described herein, is a datum describingthe availability of the airport, wherein the availability of the airportmay include the availability of an aircraft divided by type of aircraft,time of availability, conditions at the airport, and the like. Forexample and without limitation, the airport metrics datum may include 2Cessna 172 aircraft available from 12 pm to 4 pm. As a further exampleand without limitation, the airport metrics datum may include one Bell206 JetRanger available from 9 am to 11 am. Further, in an embodiment,scheduler module 712 and/or flight controller 120 may include receivingat least a simulator metrics datum from a flight simulator 128. The“simulator metrics datum” is a datum describing the availability offlight simulator 128, wherein the availability may include the location,timing, and types of flights available. For example and withoutlimitation, the simulator metrics datum may include one flight simulatoravailable at the Burlington International Airport in a specific hangarfrom 1 pm to 6 pm utilizing a simulated Boeing 737 flight. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various availability data which may be suitable for use asairport metrics datum and/or simulator metrics datum consistently withthis disclosure.

With continued reference to FIG. 7 , scheduler module 712 operating onflight management engine 700, and/or flight controller 120 may befurther configured to receive a pilot restriction from user device 148.User device 148 may include any user device 148 as described in theentirety of this disclosure. The “pilot restriction” as described in theentirety of this disclosure is a datum detailing the most advancedflying capabilities of the user and/or user device 148, wherein thepilot restriction correlates to the pilot license, endorsement, and/orcapabilities of the user and/or user device 148. For example and withoutlimitation, the pilot restriction may include endorsement dataindividual to the pilot, such as student, solo, rotorcraft solo, lightsport, private pilot, instrument, complex, multi-engine, highperformance, tail wheel, sea plane, rotorcraft, powered lift,commercial, ATP, VTOL, eVTOL, and the like. As a further example andwithout limitation, the pilot restriction may include the need for aninstructor to be present on each flight performed by the pilot. Further,the pilot restriction may include, as a non-limiting example, aircraftclassifications, such as fixed wing, rotary wing, VTOL, and the like.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various endorsements and/or capabilitiesrestricting flight which may be suitable for use as the pilotrestriction consistently with this disclosure.

Referring now to FIG. 8 , an embodiment of registry module 704 operatingon flight management engine 700, as described in 7, is illustrated indetail. In an embodiment, registry module 140 and/or flight controller120 may be configured to receive a plurality of maneuver data 800 frominstructor device 416 and record each maneuver data of the plurality ofmaneuver data 800 in pilot log database 716. Plurality of maneuver data800 may include any maneuver data as described in the entirety of thisdisclosure. For example and without limitation, the plurality ofmaneuver data may include foundational flight maneuvers, such asstraight-and-level turns, climbs and descents, and/or performancemaneuvers, such that the application of flight control pressures,attitudes, airspeeds, and orientations are constantly changingthroughout the maneuver. Registry module 140 and/or flight controller120 may be further configured to receive a plurality of tactile movementdata 804 from sensor 704 disposed on electric aircraft 152, as describedin further detail above in reference to FIG. 7 . Plurality of tactilemovement data 804 may include any tactile movement data as described inthe entirety of this disclosure. For example and without limitation,tactile movement data may include a measurement of the heading ofelectric aircraft 152 and/or flight simulator 128 during a flight, thepitch attitude of electric aircraft 152 and/or flight simulator 128during a flight, the bank attitude of electric aircraft 152 and/orflight simulator 128 during a flight, the airspeed of electric aircraft152 and/or flight simulator 128 during a flight, the speed of rotorrotation of electric aircraft 152 and/or flight simulator 128 during aflight, the positioning of electric aircraft 152 and/or flight simulator128 during a flight, and the like.

With continued reference to FIG. 8 , registry module 140 and/or flightcontroller 120 may be configured to select training set 708 as afunction of the plurality of tactile movement data 804, wherein thetraining set includes plurality of maneuver data 800 correlated to anelement of flight movement data, as described above in further detail inreference to FIG. 7 . The element of flight movement data may includeany element of flight movement data as described in the entirety of thisdisclosure. For example and without limitation, flight movement data mayinclude any data indicative of a pilot's maneuver performed and/orattempted to perform, such as landing data, takeoff data, turn data,instrumentation data, emergency protocol data, stall data, and the like.Selection of training set 708 as a function of the plurality of tactilemovement data 804 may be performed utilizing any means of selection asdescribed in the entirety of this disclosure. For example and withoutlimitation, selecting training set 708 includes correlating the tactilemovement data 804 to an element of flight movement data, and selectingthe training set 708 as a function of the correlation. In an embodiment,the correlation may be recorded when an element of flight movement dataand tactile movement data 804 are simultaneously performed, such thatthe element of flight movement data and the tactile movement data 804occur during the same moment of a flight utilizing electric aircraft 152and/or simulation machine 128; tactile movement data 804 and/or theelement of flight movement data may be received by flight controller 120during a flight, utilizing electric aircraft 152 and/or simulationmachine 128, and recorded in data entries, such that the data entriesmay be correlated and used in training data. For example and withoutlimitation, tactile movement data 804 may include data detailing a turninvolving a bank of 32 degrees and may be correlated to an element offlight movement data detailing steep turns, wherein steep turns requirea bank angle or more than 30 degrees. As a further example and withoutlimitation, tactile movement data 804 may include data describing a 30second hover at 6500 feet and may be correlated to an element of flightmovement data describing hovering flight, wherein hovering requires amaintained position and altitude for an extended duration of time.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various means of selection which may besuitable for use with the selection of training set 708 consistentlywith this disclosure.

Still referring to FIG. 8 , in each first data element of training set708, at least a first maneuver data of the plurality of maneuver data800 of the data element may be correlated with element of flightmovement data of the data element. In an embodiment, an element offlight movement data may be correlated with each maneuver data of theplurality of maneuver data 800 where the element of flight movement datais located in the same data element and/or portion of data element aseach maneuver data of the plurality of maneuver data 800; for example,and without limitation, an element of flight movement data may becorrelated with each maneuver data of the plurality of maneuver data 800where both element of flight maneuver data and each maneuver data of theplurality of maneuver data 800 are contained within the same first dataelement of the training set 708. As a further example, an element offlight movement data may be correlated with each maneuver data of theplurality of maneuver data 800 where both share a category label asdescribed in further detail below, where each is within a certaindistance of the other within an ordered collection of data in dataelement, or the like. Still further, an element of flight movement datamay be correlated with each maneuver data of the plurality of maneuverdata 800 where the element of flight movement data and each maneuverdata of the plurality of maneuver data 800 share an origin, such asbeing data that was collected with regard to a single person or thelike. In an embodiment, a first datum may be more closely correlatedwith a second datum in the same data element than with a third datumcontained in the same data element; for instance, the first element andthe second element may be closer to each other in an ordered set of datathan either is to the third element, the first element and secondelement may be contained in the same subdivision and/or section of datawhile the third element is in a different subdivision and/or section ofdata, or the like. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various forms and/ordegrees of correlation between flight movement data and maneuver datathat may exist in training set 708 and/or first data elementconsistently with this disclosure.

In an embodiment, and still referring to FIG. 8 , registry module 140operating on flight management engine 700 and/or flight controller 120may be designed and configured to associate at least an element offlight movement data with at least a category from a list of significantcategories of flight movement data. Significant categories of flightmovement data may include labels and/or descriptors describing types offlight movement data that are identified as being of high relevance inidentifying each maneuver data of the plurality of maneuver data 800. Asa non-limiting example, one or more categories may identify significantcategories of flight movement data based on degree of relevance to oneor more impactful positions and/or within one or more aircraft types.For instance, and without limitation, a particular set of angle of bank,speed, and/or altitude information may be recognized in a given aircrafttype as useful for identifying various types of turns within a relevantfield. As a non-limiting example, and without limitation, flightmovement data describing the movement of a left turn, such as anincrease in the angle of aircraft bank to the left, an increase in thestall speed, and an increase in the angle of attack of the aircraft nosemay be recognized as useful for identifying various turns such a shallowleft turn, a medium left turn, steep left turn, and the like. As anadditional example, the deployment of landing gear may be useful foridentifying takeoff procedures and/or techniques and/or ground referencemaneuvers, such as turns around a point, s-turns, the rectangularcourse, taxiing and the like. In a further non-limiting example, anattitude of zero degrees may be useful for identifying hovering,straight and level flight, the initiation of vertical landing,initiation of vertical takeoff, or the like. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousadditional categories of flight movement data that may be usedconsistently with this disclosure.

Still referring to FIG. 8 , registry module 704 operating on flightmanagement engine 700 and/or flight controller 120 may receive the listof significant categories according to any suitable process; forinstance, and without limitation, registry module 704 may receive thelist of significant categories from at least an expert. In anembodiment, registry module 704 and/or a user device connected toregistry module 704 may provide a graphical user interface, which mayinclude without limitation a form or other graphical element having dataentry fields, wherein one or more experts, including without limitationaviation and/or pilot experts, may enter information describing one ormore categories of flight movement that the experts consider to besignificant or useful for detection of maneuvers. Fields in graphicaluser interface may provide options describing previously identifiedcategories, which may include a comprehensive or near-comprehensive listof types of flight movement data detectable using known or recordedmethods, for instance in “drop-down” lists, where experts may be able toselect one or more entries to indicate their usefulness and/orsignificance in the opinion of the experts. Fields may include free-formentry fields such as text-entry fields where an expert may be able totype or otherwise enter text, enabling expert to propose or suggestcategories not currently recorded. The graphical user interface or thelike may include fields corresponding to each maneuver data of theplurality of maneuver data 800, where experts may enter data describingmaneuver data 800 and/or categories of maneuver data 800 the expertsconsider related to entered categories of flight movement data; forinstance, such fields may include drop-down lists or other pre-populateddata entry fields listing currently recorded each maneuver data of theplurality of maneuver data 800, and which may be comprehensive,permitting each expert to select each maneuver data of the plurality ofmaneuver data 800 and/or a plurality of maneuver data 800 the expertbelieves to be predicted and/or associated with each category of flightmovement data selected by the expert. Fields for entry of maneuver dataand/or categories of plurality of maneuver data 800 may includefree-form data entry fields such as text entry fields; as describedabove, experts may enter data not presented in pre-populated data fieldsin the free-form data entry fields. Alternatively or additionally,fields for entry of each maneuver data of the plurality of maneuver data800 may enable an expert to select and/or enter information describingor linked to a category of maneuver data that the expert considerssignificant, where significance may indicate likely impact oncompleteness, accuracy, safety, timing, or the like as described infurther detail below. The graphical user interface may provide an expertwith a field in which to indicate a reference to a document describingsignificant categories of flight movement data, relationships of suchcategories to each maneuver data of the plurality of maneuver data 800,and/or significant categories of plurality of maneuver data 800. Anydata described above may alternatively or additionally be received fromexperts similarly organized in paper form, which may be captured andentered into data in a similar way, or in a textual form such as aportable document file (PDF) with examiner entries, or the like.

Continuing to refer to FIG. 8 , data information describing significantcategories of flight movement data, relationships of such categories toeach maneuver data of the plurality of maneuver data 800, and/orsignificant categories of the plurality of maneuver data 800 mayalternatively or additionally be extracted from one or more documentsusing a language processing module 808. Language processing module 808may include any hardware and/or software module. Language processingmodule 808 and/or flight controller 120 may be configured to extract,from the one or more documents, one or more words. One or more words mayinclude, without limitation, strings of one or characters, includingwithout limitation any sequence or sequences of letters, numbers,punctuation, diacritic marks, engineering symbols, geometricdimensioning and tolerancing (GD&T) symbols, chemical symbols andformulas, spaces, whitespace, and other symbols, including any symbolsusable as textual data as described above. Textual data may be parsedinto tokens, which may include a simple word (sequence of lettersseparated by whitespace) or more generally a sequence of characters asdescribed previously. The term “token,” as used herein, refers to anysmaller, individual groupings of text from a larger source of text;tokens may be broken up by word, pair of words, sentence, or otherdelimitation. These tokens may in turn be parsed in various ways.Textual data may be parsed into words or sequences of words, which maybe considered words as well. Textual data may be parsed into “n-grams”,where all sequences of n consecutive characters are considered. Any orall possible sequences of tokens or words may be stored as “chains”, forexample for use as a Markov chain or Hidden Markov Model.

Still referring to FIG. 8 , language processing module 808 and/or flightcontroller 120 may compare extracted words to categories of flightmovement data recorded at registry module 704 and/or flight managementengine 700, one or more maneuver data of the plurality of maneuver data800 recorded at registry module 704 and/or flight management engine 700,and/or one or more categories of plurality of maneuver data 800 recordedat registry module 704 and/or flight management engine 700; such datafor comparison may be entered on registry module 704 and/or flightmanagement engine 700 as described above using expert data inputs or thelike. In an embodiment, one or more categories may be enumerated, tofind total count of mentions in such documents. Alternatively oradditionally, language processing module 808 and/or flight controller120 may operate to produce a language processing model. Languageprocessing model may include a program automatically generated byregistry module 704, flight management engine 700 and/or languageprocessing module 808 to produce associations between one or more wordsextracted from at least a document and detect associations, includingwithout limitation mathematical associations, between such words, and/orassociations of extracted words with categories of flight movement data,relationships of such categories to each maneuver data of the pluralityof maneuver data 800, and/or categories of plurality of maneuver data800. Associations between language elements, where language elementsinclude for purposes herein extracted words, categories of flightmovement data, relationships of such categories to each maneuver data ofthe plurality of maneuver data 800, and/or categories of plurality ofmaneuver data 800 may include, without limitation, mathematicalassociations, including without limitation statistical correlationsbetween any language element and any other language element and/orlanguage elements. Statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating, for instance, a likelihood that a given extracted wordindicates a given category of flight movement data, a given relationshipof such categories to plurality of maneuver data 800, and/or a givencategory of plurality of maneuver data 800. As a further example,statistical correlations and/or mathematical associations may includeprobabilistic formulas or relationships indicating a positive and/ornegative association between at least an extracted word and/or a givencategory of flight movement data, a given relationship of suchcategories to plurality of maneuver data 800, and/or a given category ofplurality of maneuver data 800; positive or negative indication mayinclude an indication that a given document is or is not indicating acategory of flight movement data, a given relationship of suchcategories to plurality of maneuver data 800, and/or a given category ofplurality of maneuver data 800 is or is not significant. For instance,and without limitation, a negative indication may be determined from aphrase such as “angle of attach of the nose of the aircraft was notfound to be an accurate predictor of turn type,” whereas a positiveindication may be determined from a phrase such as “angle of attack ofthe nose of the aircraft was found to be a positive indicator of asuccessful approach to fixed-wing landing,” as an illustrative example;whether a phrase, sentence, word, or other textual element in a documentor corpus of documents constitutes a positive or negative indicator maybe determined, in an embodiment, by mathematical associations betweendetected words, comparisons to phrases and/or words indicating positiveand/or negative indicators that are stored in memory at registry module704, flight management engine 700, and/or the like.

Still referring to FIG. 8 , language processing module 808, registrymodule 704, flight management engine 700 and/or flight controller 120may generate the language processing model by any suitable method,including without limitation a natural language processingclassification algorithm; language processing model may include anatural language process classification model that enumerates and/orderives statistical relationships between input term and output terms.Algorithm to generate language processing model may include a stochasticgradient descent algorithm, which may include a method that iterativelyoptimizes an objective function, such as an objective functionrepresenting a statistical estimation of relationships between terms,including relationships between input terms and output terms, in theform of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMIs, as usedherein, are statistical models with inference algorithms that that maybe applied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted word category ofphysiological data, a given relationship of such categories toprognostic labels, and/or a given category of prognostic labels. Theremay be a finite number of category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels to which an extracted word may pertain; anHMM inference algorithm, such as the forward-backward algorithm or theViterbi algorithm, may be used to estimate the most likely discretestate given a word or sequence of words. Language processing module 808and/or flight controller 120 may combine two or more approaches. Forinstance, and without limitation, machine-learning program may use acombination of Naive-Bayes (NB), Stochastic Gradient Descent (SGD), andparameter grid-searching classification techniques; the result mayinclude a classification algorithm that returns ranked associations.

Continuing to refer to FIG. 8 , generating language processing model 808may include generating a vector space, which may be a collection ofvectors, defined as a set of mathematical objects that can be addedtogether under an operation of addition following properties ofassociativity, commutativity, existence of an identity element, andexistence of an inverse element for each vector, and can be multipliedby scalar values under an operation of scalar multiplication compatiblewith field multiplication, and that has an identity element isdistributive with respect to vector addition, and is distributive withrespect to field addition. Each vector in an n-dimensional vector spacemay be represented by an n-tuple of numerical values. Each uniqueextracted word and/or language element as described above may berepresented by a vector of the vector space. In an embodiment, eachunique extracted and/or other language element may be represented by adimension of vector space; as a non-limiting example, each element of avector may include a number representing an enumeration ofco-occurrences of the word and/or language element represented by thevector with another word and/or language element. Vectors may benormalized, scaled according to relative frequencies of appearanceand/or file sizes. In an embodiment associating language elements to oneanother as described above may include computing a degree of vectorsimilarity between a vector representing each language element and avector representing another language element; vector similarity may bemeasured according to any norm for proximity and/or similarity of twovectors, including without limitation cosine similarity, which measuresthe similarity of two vectors by evaluating the cosine of the anglebetween the vectors, which can be computed using a dot product of thetwo vectors divided by the lengths of the two vectors. Degree ofsimilarity may include any other geometric measure of distance betweenvectors.

Still referring to FIG. 8 , language processing module 808 and/or flightcontroller 120 may use a corpus of documents to generate associationsbetween language elements in a language processing module 808, registrymodule 704 and/or flight management engine 700 may then use suchassociations to analyze words extracted from one or more documents anddetermine that the one or more documents indicate significance of acategory of flight movement data, a given relationship of suchcategories to each maneuver data of the plurality of maneuver data 800,and/or a given category of plurality of maneuver data 800. In anembodiment, registry module 704 and/or flight management engine 700 mayperform this analysis using a selected set of significant documents,such as documents identified by one or more experts as representing goodscience, good aviation analysis, good aviation standard, or the like;experts may identify or enter such documents via graphical userinterface as described above, or may communicate identities ofsignificant documents according to any other suitable method ofelectronic communication, or by providing such identity to other personswho may enter such identifications into registry module 704 and/orflight management engine 700. Documents may be entered into registrymodule 704 and/or flight management engine 700 by being uploaded by anexpert or other persons using, without limitation, file transferprotocol (FTP) or other suitable methods for transmission and/or uploadof documents; alternatively or additionally, where a document isidentified by a citation, a uniform resource identifier (URI), uniformresource locator (URL) or other datum permitting unambiguousidentification of the document, registry module 704 and/or flightmanagement engine 700 may automatically obtain the document using suchan identifier, for instance by submitting a request to a database orcompendium of documents such as JSTOR as provided by Ithaka Harbors,Inc. of New York.

Continuing to refer to FIG. 8 , whether an entry indicating significanceof a category of flight movement data, a given relationship of suchcategories to each maneuver data of the plurality of maneuver data 800,and/or a given category of plurality of maneuver data 800 is entered viagraphical user interface, alternative submission means, and/or extractedfrom a document or body of documents as described above, an entry orentries may be aggregated to indicate an overall degree of significance.For instance, each category of flight movement data, a givenrelationship of such categories to each maneuver data of the pluralityof maneuver data 800, and/or a given category of plurality of maneuverdata 800 may be given an overall significance score; overallsignificance score may, for instance, be incremented each time an expertsubmission and/or paper indicates significance as described above.Persons skilled in the art, upon reviewing the entirety of thisdisclosure will be aware of other ways in which scores may be generatedusing a plurality of entries, including averaging, weighted averaging,normalization, and the like. Significance scores may be ranked; that is,all category of flight movement data, a given relationship of suchcategories to each maneuver data of the plurality of maneuver data 800,and/or a given category of plurality of maneuver data 800 may be rankedaccording significance scores, for instance by ranking category offlight movement data, a given relationship of such categories to eachmaneuver data of the plurality of maneuver data 800, and/or a givencategory of plurality of maneuver data 800 higher according to highersignificance scores and lower according to lower significance scores.Categories of flight movement data, a given relationship of suchcategories to each maneuver data of the plurality of maneuver data 800,and/or a given category of plurality of maneuver data 800 may beeliminated from current use if they fail a threshold comparison, whichmay include a comparison of significance score to a threshold number, arequirement that significance score belong to a given portion of rankingsuch as a threshold percentile, quartile, or number of top-rankedscores. Significance scores may be used to filter outputs as describedin further detail below; for instance, where a number of outputs aregenerated and automated selection of a smaller number of outputs isdesired, outputs corresponding to higher significance scores may beidentified as more probable and/or selected for presentation while otheroutputs corresponding to lower significance scores may be eliminated.

With continued reference to FIG. 8 , registry module 704 operating onflight management engine 700 and/or flight controller 120 may includemachine-learning module 812. Machine-learning module 812 can be designedand configured to generate a maneuver model output based on theplurality of tactile movement data 804 and the selected training set708. The maneuver model output can be configured to include eachmaneuver data of the plurality of maneuver data 800 for each tacticalmovement data of the plurality of tactical movement data 804.Machine-learning module 812 may include any hardware and/or softwaremodule. Machine-learning module 812 can be designed and configured togenerate outputs using machine learning processes. As discussed above, amachine learning process is a process that automatedly uses a body ofdata known as “training data” and/or a “training set” to generate analgorithm that will be performed by a computing device/module to produceoutputs given data provided as inputs; this is in contrast to anon-machine learning software program where the commands to be executedare determined in advance by a user and written in a programminglanguage.

Still referring to FIG. 8 , machine-learning module 812 may be designedand configured to generate a machine-learning model, wherein themachine-learning model is at least a model that determines amathematical relationship between flight movement data and plurality ofmovement data 800. The machine-learning model may be configured toreceive an element of flight movement data and each maneuver data of theplurality of maneuver data 800 as an input and output a maneuver modeloutput based on the correlations in the selected training set 708. Suchmodels may include without limitation model developed using linearregression models. Linear regression models may include ordinary leastsquares regression, which aims to minimize the square of the differencebetween predicted outcomes and actual outcomes according to anappropriate norm for measuring such a difference (e.g. a vector-spacedistance norm); coefficients of the resulting linear equation may bemodified to improve minimization. Linear regression models may includeridge regression methods, where the function to be minimized includesthe least-squares function plus term multiplying the square of eachcoefficient by a scalar amount to penalize large coefficients. Linearregression models may include least absolute shrinkage and selectionoperator (LASSO) models, in which ridge regression is combined withmultiplying the least-squares term by a factor of 1 divided by doublethe number of samples. Linear regression models may include a multi-tasklasso model wherein the norm applied in the least-squares term of thelasso model is the Frobenius norm amounting to the square root of thesum of squares of all terms. Linear regression models may include theelastic net model, a multi-task elastic net model, a least angleregression model, a LARS lasso model, an orthogonal matching pursuitmodel, a Bayesian regression model, a logistic regression model, astochastic gradient descent model, a perceptron model, a passiveaggressive algorithm, a robustness regression model, a Huber regressionmodel, or any other suitable model that may occur to persons skilled inthe art upon reviewing the entirety of this disclosure. Linearregression models may be generalized in an embodiment to polynomialregression models, whereby a polynomial equation (e.g. a quadratic,cubic or higher-order equation) providing a best predicted output/actualoutput fit is sought; similar methods to those described above may beapplied to minimize error functions, as will be apparent to personsskilled in the art upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 8 , a machine-learning algorithm used togenerate a machine-learning model may include, without limitation,linear discriminant analysis. Machine-learning algorithm may includequadratic discriminate analysis. Machine-learning algorithms may includekernel ridge regression. Machine-learning algorithms may include supportvector machines, including without limitation support vectorclassification-based regression processes. Machine-learning algorithmsmay include stochastic gradient descent algorithms, includingclassification and regression algorithms based on stochastic gradientdescent. Machine-learning algorithms may include nearest neighborsalgorithms. Machine-learning algorithms may include Gaussian processessuch as Gaussian Process Regression. Machine-learning algorithms mayinclude cross-decomposition algorithms, including partial least squaresand/or canonical correlation analysis. Machine-learning algorithms mayinclude naïve Bayes methods. Machine-learning algorithms may includealgorithms based on decision trees, such as decision tree classificationor regression algorithms. Machine-learning algorithms may includeensemble methods such as bagging meta-estimator, forest of randomizedtress, AdaBoost, gradient tree boosting, and/or voting classifiermethods. Machine-learning algorithms may include neural net algorithms,including convolutional neural net processes.

Still referring to FIG. 8 , machine-learning module 812 may generatemaneuver model output using alternatively or additional artificialintelligence methods, including without limitation by creating anartificial neural network, such as a convolutional neural networkcomprising an input layer of nodes, one or more intermediate layers, andan output layer of nodes. Connections between nodes may be created viathe process of “training” the network, in which elements from a trainingdataset are applied to the input nodes, a suitable training algorithm(such as Levenberg-Marquardt, conjugate gradient, simulated annealing,or other algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning. This network may be trained using training set 708;the trained network may then be used to apply detected relationshipsbetween elements of flight movement data and each maneuver data of theplurality of maneuver data 800.

With continued reference to 8, machine-learning algorithms used bymachine-learning module 812 may include supervised machine-learningalgorithms, which may, as a non-limiting example be executed using aregistry module 704 executing on flight management engine 700 and/or onanother computing device in communication with flight controller 120,which may include any hardware or software module. Supervised machinelearning algorithms, as defined herein, include algorithms that receivea training set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm may useelements of flight movement data as inputs, each maneuver data of theplurality of maneuver data 800 as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweenelements of flight movement data and each maneuver data of the pluralityof maneuver data 800; scoring function may, for instance, seek tomaximize the probability that a given element of flight movement dataand/or combination of elements of flight movement data is associatedwith a given maneuver data of the plurality of maneuver data 800 and/orcombination of plurality of maneuver data 800 to minimize theprobability that a given element of flight movement data and/orcombination of elements of flight movement data is not associated with agiven maneuver data of the plurality of maneuver data 800 and/orcombination of plurality of maneuver data 800. A scoring function may beexpressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training set 70S. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various possiblevariations of supervised machine learning algorithms that may be used todetermine relation between elements of flight movement data and eachmaneuver data of the plurality of maneuver data 800, In an embodiment,one or more supervised machine-learning algorithms may be restricted toa particular domain for instance, a supervised machine-learning processmay be performed with respect to a given set of parameters and/orcategories of parameters that have been suspected to be related to agiven set of maneuver data of the plurality of maneuver data 800. and/orare specified as linked to an aircraft type and/or aircraft classcovering a particular set of plurality of maneuver data 800. As anon-limiting example, a particular pitch attitudes and/or sensor datamay be typically used by rotorcraft and/or VTOL pilots to specify aparticular landing technique, and a supervised machine-learning processmay be performed to relate those particular pitch attitudes and/orsensor data to the various landing techniques; in an embodiment, domainrestrictions of supervised machine-learning procedures may improveaccuracy of resulting models by ignoring artifacts in training data.Domain restrictions may be suggested by experts and/or deduced fromknown purposes for particular evaluations and/or known tests used toevaluate each maneuver data of the plurality of maneuver data 800.Additional supervised learning processes may be performed without domainrestrictions to detect, for instance, previously unknown and/orunsuspected relationships between flight movement data and each maneuverdata of the plurality of maneuver data 800.

Still referring to FIG. 8 , machine-learning module 812 mayalternatively or additionally be designed and configured to generate themaneuver model output by executing a lazy learning process as a functionof the selected training set 708 and the plurality of tactile movementdata 804; lazy learning processes may be executed using a registrymodule 704 executing on flight management engine 700 and/or on anothercomputing device in communication with flight controller 120, which mayinclude any hardware or software module. A lazy-learning process and/orprotocol, which may alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover a “first guess” ata prognostic label associated with biological extraction, using selectedtraining set 708. As a non-limiting example, an initial heuristic mayinclude a ranking of each maneuver data of the plurality of maneuverdata 800 according to relation to an aircraft class of the plurality oftactile movement data, one or more categories of flight movement dataidentified in test type of the plurality of tactile movement data,and/or one or more values detected in the plurality of tactile movementdata; ranking may include, without limitation, ranking according tosignificance scores of associations between elements of flight movementdata and each maneuver data of the plurality of maneuver data 800, forinstance as calculated as described above. Heuristic may includeselecting some number of highest-ranking associations and/or pluralityof maneuver data 800. Supervised machine-learning model 812 mayalternatively or additionally implement any suitable “lazy learning”algorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate maneuver modeloutputs as described in this disclosure, including without limitationlazy learning applications of machine-learning algorithms as describedin further detail below. Machine-learning module 812 may be configuredto include any machine-learning algorithm and/or machine-learningprocess as described in further detail in the entirety of thisdisclosure.

With continued reference to FIG. 8 , registry module 704 operating onflight management engine 700 and/or flight controller 120 may beconfigured to record each maneuver data of the plurality of maneuverdata 800 in pilot log database 716. Registry module 704 and/or flightcontroller 120 may be further configured, in an embodiment, to storemaneuver model output in pilot log database 716. Flight managementengine 700 and/or registry module 704 may include or communicate withpilot log database 716. Pilot log database 716 may be implemented as anydatabase and/or datastore suitable for use as pilot log database 716 asdescribed in the entirety of this disclosure. Each maneuver data of theplurality of maneuver data may be recorded in any suitable data and/ordata type, as described above in reference to FIG. 7 .

Continuing to refer to FIG. 8 , registry module 704 operating on flightmanagement engine 700 and/or flight controller 120 may be configured torecord a flight mapping datum utilizing tracking unit 816 disposed onelectric aircraft 152. Tracking unit 816 may be disposed internallyand/or externally on electric aircraft 152. The “tracking unit” asdescribed herein, is a satellite navigation device wherein the device isconfigured to calculate the geographical position of electric aircraft152. For example and without limitation, tracking unit 816 may includedata loggers, data pushers, data pullers, automatic dependentsurveillance-broadcast, any combination thereof, and/or the like.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various satellite navigation devices thatmay be used as tracking unit 816 consistently with this disclosure. The“flight mapping datum” as described in this disclosure, is a datumcontaining the geographical positioning of electric aircraft 152. Forexample and without limitation, the flight mapping datum may includeunique identifiers of the precise geographic location of electricaircraft 152 on Earth, such as longitude and latitude. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various geographical positioning that may be used as the flightmapping datum consistently with this disclosure. Registry module 704and/or flight controller 120 operating on flight management engine 700may be configured to store the flight mapping datum in pilot logdatabase 716. Pilot log database 716 may be implemented as any databaseand/or datastore suitable for use as pilot log database 716 as describedin the entirety of this disclosure. The flight mapping datum may bestored and/or recorded in any suitable data and/or data type, asdescribed in the entirety of this disclosure. There is no limitation onforms textual data or non-textual data the flight mapping datum maytake; persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various forms which may be suitable for useas the flight mapping datum consistently with this disclosure.

Referring now to FIG. 9 , an embodiment of maneuver database 720 isillustrated. Maneuver database 720 may include any data structure forordered storage and retrieval of data, which may be implemented as ahardware or software module. Maneuver database 720 may be implemented,without limitation, as a relational database, a key-value retrievaldatastore such as a NOSQL database, or any other format or structure foruse as a datastore that a person skilled in the art would recognize assuitable upon review of the entirety of this disclosure. Maneuverdatabase 720 may include a plurality of data entries and/or recordscorresponding to elements of flight movement data as described above.Data entries and/or records may describe, without limitation, dataconcerning particular aircraft procedures, techniques, and skills thathave been collected by electric aircraft 152. Data entries in a maneuverdatabase 720 may be flagged with or linked to one or more additionalelements of information, which may be reflected in data entry cellsand/or in linked tables such as tables related by one or more indices ina relational database; one or more additional elements of informationmay include data associating a flight procedure with one or morecohorts, including aircraft type groupings, such as fixed conventional,fixed wing complex, light sport, private pilot, instrument, complex,multi-engine, high performance, tail wheel, sea plane, rotorcraft,powered lift, commercial, VTOL, eVTOL, or the like. Additional elementsof information may include one or more categories of flight movementdata as described above. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in which dataentries in a maneuver database 720 may reflect categories, cohorts,and/or populations of data consistently with this disclosure.

Still referring to FIG. 9 , one or more database tables in maneuverdatabase 720 may include, as a non-limiting example, a takeoff datatable 900. Takeoff data table 900 may be a table storing takeoff data offlight movement data. Takeoff data table 900 may further include,without limitation, correlating a plurality of tactile movement data 804received from a device, such as sensor 704 of electric aircraft 152, asdescribed above, to takeoff data of flight movement data. For instance,and without limitation, maneuver database 720 may include a takeoff datatable 900 listing takeoff data such as runway alignment, threshold oftakeoff pitch attitude, threshold of takeoff bank attitude, threshold oftakeoff heading of the aircraft, threshold of takeoff airspeed,threshold of rotor speed for vertical takeoff, and the like, asdescribed above in further detail in reference to FIG. 7 .

With continued reference to FIG. 9 , maneuver database 720 may include,as a non-limiting example, an instrumentation data table 904. Forinstance, and without limitation, maneuver database 720 may include aninstrumentation data table 904 listing instrumentation data, such as athreshold of vertical speed, threshold of attitude, threshold ofaltimeter, threshold of airspeed of horizontal flight, and the like, asdescribed above in further detail in reference to FIG. 7 . As anothernon-limiting example, maneuver database 720 may include a turn datatable 908, which may list turn data, such as threshold of bank angle fora shallow turn, threshold of bank angle for a medium turn, threshold ofbank angle for steep turns, threshold of yaw in the direction of theturn, threshold of yaw in the direction opposite the turn, threshold ofairspeed during turn, threshold of heading of the aircraft during turns,and the like, as described above in further detail in reference to FIG.7 . As a further non-limiting example, maneuver database 720 may includea landing data table 912, which may list landing data, such as runwayalignment, threshold angle of vertical descent, threshold of angle oflanding, threshold of landing pitch attitude, threshold of landing bankattitude, threshold of heading of the aircraft during landing, thresholdof landing airspeed, threshold of vibrational frequency during landing,threshold of rotor speed for vertical landing, and the like, asdescribed in further detail above in reference to FIG. 7 . As a furtherexample, also non-limiting, maneuver database 720 may include anemergency protocol data table 916, which may list emergency protocoldata, such as a threshold of time to recovery of a system malfunctionand/or failure, such as power failure in a rotor, power failure in apropeller, damage to a wing, damage to the fuselage, dynamic rollover,damage to a rotor, malfunction with the collective, malfunction with theinceptor stick, and the like, threshold attitude during a malfunction,threshold level of hover, and the like, as described above in furtherdetail in reference to FIG. 7 . Tables presented above are presented forexemplary purposes only; persons skilled in the art will be aware ofvarious ways in which data may be organized in maneuver database 720consistently with this disclosure.

Referring now to FIG. 10 , an embodiment of scheduler module 712operating on flight management engine 700 is illustrated in detail. Inan embodiment, scheduler module 712 and/or flight controller 120 may beconfigured to receive an airport metrics datum 1000 from centralauthority 316, as described in further detail above in reference to FIG.7 . Airport metrics datum 1000 may include any airport metrics datum asdescribed in the entirety of this disclosure. For example and withoutlimitation, the airport metrics datum may include 2 Cessna 172 aircraftavailable from 12 pm to 4 pm. As a further example and withoutlimitation, the airport metrics datum may include one Bell 206 JetRangeravailable from 9 am to 11 am. Scheduler module 712 and/or flightcontroller 120 may be further configured to receive a pilot restriction1004 from user device 148, as described in further detail above inreference to FIG. 7 . Pilot restriction 1004 may include any pilotrestriction as described in the entirety of this disclosure. For exampleand without limitation, the pilot restriction may include endorsementdata individual to the pilot, such as student, solo, rotorcraft solo,light sport, private pilot, instrument, complex, multi-engine, highperformance, tail wheel, sea plane, rotorcraft, powered lift,commercial, ATP, VTOL, eVTOL, and the like. As a further example andwithout limitation, the pilot restriction may include the need for aninstructor to be present on each flight performed by the pilot.

Still referring to FIG. 10 , scheduler module 712 operating on flightmanagement engine 700 and/or flight controller 120 may be configured toselect a correlated dataset 1008 containing a plurality of data entrieswherein each dataset 1008 contains a datum of pilot data and at least acorrelated compatible aircraft marker as a function of pilot restriction1004. In an embodiment, the correlation may occur when the element ofairport metrics datum and the correlated compatible aircraft marker arethe same, such that the aircraft available at an airport and theaircraft able to be piloted by the user and/or user device 148 are thesame. In the embodiment, the airport metrics datum and the pilotrestriction may be received by flight controller 120 from centralauthority 316 and user device 148 respectively, and recorded in dataentries, such that the data entries may be correlated. Pilot restriction1004 may include any pilot restriction as described in the entirety ofthis disclosure. Datasets may be selected and contained within aircraftmarker database 1012. Flight management engine 700 and/or schedulermodule 712 and/or flight controller 120 may include or communicate withaircraft marker database 1012. Aircraft marker database 1012 may beimplemented as any database and/or datastore suitable for use asaircraft marker database 1012 as described in the entirety of thisdisclosure. An exemplary embodiment of aircraft marker database 1012 isincluded below in reference to FIG. 11 .

With continued reference to FIG. 10 , each dataset contains a datum ofpilot data and a correlated compatible aircraft marker. Correlatedcompatible aircraft marker, as described in the entirety of thisdisclosure, is any element of data identifying and/or describing theaircraft able to be piloted by the user and/or user device 148 in viewof the flying capabilities of the user, as provided by the pilotrestriction. For example and without limitation, the aircraft markercorrelates the aircraft capable of flight by the user and/or user device148 given the pilot's licenses, certifications, and endorsements. Forexample and without limitation, a user with an endorsement data of alight sport pilot may correlate to an aircraft marker detailing onlyaircraft including a single engine, maximum of two seats, weighing lessthan 1,320 pounds and having a maximum speed of 138 mph, such as theCessna 162 Skycatcher, Pipistrel Virus, Remos G3, and the like. As afurther example and without limitation, a user with endorsement data ofa private pilot may correlate to an aircraft marker including allfixed-wing single engine aircraft operating at less than 180 horsepower.A user with endorsement data of powered lift may correlate, as anon-limiting example, to an aircraft marker detailing heavier-than-airaircraft capable of vertical takeoff, vertical landing, and low speedflight that depends principally on engine-driven lift devices or enginethrust for lift during these flight regimes and on nonrotatingairfoil(s) for lift during horizontal flight. Persons skilled in theart, upon reviewing the entirety of this disclosure, will be aware ofvarious types of aircraft which may be suitable for use as the aircraftmarker consistently with this disclosure. In an embodiment, correlateddataset 1008 may include any data suitable for use as training data asdescribed in the entirety of this disclosure.

Continuing to refer to FIG. 10 , scheduler module 712 operating onflight management engine 700 and/or flight controller 120 may beconfigured to include a machine-learning module 1016. Machine-learningmodule 1016 may operate on the flight management engine 700, flightcontroller 120 and/or another computing device in communication with theflight controller 120, which may include any hardware and/or softwaremodule. Machine-learning module 1016 can be designed and configured togenerate outputs using machine learning processes. A machine learningprocess is a process that automatedly uses a body of data known as“dataset” and/or a “correlated dataset” to generate an algorithm thatwill be performed by a computing device/module to produce outputs givendata provided as inputs, as described above in the entirety of thisdisclosure. In an embodiment and without limitation, machine-learningmodule 1016 may include any machine-learning process and/ormachine-learning algorithm as described in the entirety of thisdisclosure. In the non-limiting embodiment, machine-learning module 1016may be implemented in any suitable means of implementation as describedin further detail in the entirety of this disclosure.

Still referring to FIG. 10 , scheduler module 712 and/or flightcontroller 120 may be configured to generate, at machine-learning module1016, a plurality of available flight output 1020 as a function ofairport metrics datum 1000 and correlated dataset 1008 utilizing amachine-learning algorithm. The “plurality of available flights” asdescribed herein, is each available flight of the plurality of availableflights, wherein each flight correlates to a possible flight option forthe pilot, the user, and/or user device 148. The possible flight optionmay include an airport within close proximity with an aircraft qualifiedfor the pilot at a specific date and time of day. For example andwithout limitation, a user in the process of achieving a rotorcraftcertificate located in Boston, Mass. may require an instructor presentfor all practice rotorcraft flights, the plurality of available flightsoutput 1020 may include the following available flights; aninstructor-led flight out of Norwood Memorial Airport in Norwood, Mass.from 10 am-1 pm on Thursday September 3^(rd) utilizing a Robinson R22helicopter, an instructor-led flight out of Norwood Memorial Airport inNorwood, Mass. from 3 pm-7 pm on Friday September 4^(th) utilizing aRobinson R22 helicopter, an instructor-led flight out of BeverlyMemorial Airport in Beverly, Mass. from 2 pm-5 pm on Friday September4^(th) utilizing a Robinson R44 helicopter, and an instructor-led flightout of Nashua Airport in Nashua, N.H. from 11 am-4 pm on FridaySeptember 4^(th) utilizing a Robinson R22 helicopter. As a furtherexample and without limitation, a user with a private pilot licenselocated in Burlington, Mass. may receive a plurality of available flightoutput 1020 including the following available flights; a flight out ofBeverly Memorial Airport in Beverly, Mass. from 9 am-12 pm on TuesdayNovember 10^(th) utilizing a Beechcraft Bonanza light aircraft, a flightout of Nashua Airport in Nashua, N.H. from 1 pm-5 pm on Tuesday November10^(th) utilizing a Cessna 172 light aircraft, a flight out of BeverlyMemorial Airport in Beverly, Mass. from 2 pm-6 pm on Wednesday November11^(th) utilizing a Cessna 172 light aircraft, and a flight out ofNorwood Memorial Airport in Norwood, Mass. from 3 pm-7 pm on WednesdayNovember 11^(th) utilizing a Beechcraft Bonanza light aircraft. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various outputs which may be suitable for use as theplurality of available flights output consistently with this disclosure.

With continued reference to FIG. 10 , scheduler module 712 operating onflight management engine 700 and/or flight controller 120 may beconfigured to display each available flight of the plurality ofavailable flights 1020 to the user and/or user device 148. The pluralityof available flights output 1020 may be displayed to the user and/oruser device 148 utilizing a graphical user interface. The graphical userinterface may include any graphical user interface as described in theentirety of this disclosure. Each available flight of the plurality ofavailable flights 1020 may be displayed to a user and/or user device148, for example and without limitation, as a textual display detailingeach available flight, an image and/or set of images detailing eachavailable flight of the plurality of available flights 1020, and/or anycombination thereof. As a further non-limiting example, the plurality ofavailable flights 1020 may be displayed to the user and/or user device148 utilizing a drop-down display detailing each available flight of theplurality of available flights 1020. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousmeans of display which may be suitable for use with the plurality ofavailable flights output consistently with this disclosure.

Continuing to refer to FIG. 10 , scheduler module 712 operating onflight management engine 700 and/or flight controller 120 may beconfigured to receive an available flight selection from the user and/oruser device 148 and transmit the available flight selection to ATCcentral authority 316. The “available flight selection” as used in thisdisclosure is the user selection of one available flight of theplurality of available flights included in output 1020, wherein the userselection includes the data associated with the available flight. Userdevice 148 may include any user device as described in this disclosure;including for example and without limitation the graphical userinterface as described above. Transmitting the available flightselection to ATC central authority 316 may further include confirmingthe available flight selected by the pilot, user, and/or user device 148with the airport and/or ATC central authority 316. ATC central authority316 may include any ATC central authority 316 as described in theentirety of this disclosure. Scheduler module 712 and/or flightcontroller 120 may be further configured to store the available flightselection in any database operating on flight controller 120, such aspilot log database 716. The available flight selection may be stored inany suitable data and/or data type, as described in the entirety of thisdisclosure. Flight management engine 700, scheduler module 712 and/orflight controller 120 may include or communicate with pilot log database716. Pilot log database 716 may be implemented as any database and/ordatastore suitable for use as pilot log database 716 as described in theentirety of this disclosure. An exemplary embodiment of pilot logdatabase 716 is included below in reference to FIG. 11 .

Referring now to FIG. 11 , an embodiment of aircraft marker database1012 is illustrated. Aircraft marker database 1012 may include any datastructure for ordered storage and retrieval of data, which may beimplemented as a hardware or software module. Aircraft marker database1012 may be implemented, without limitation, as a relational database, akey-value retrieval datastore such as a NOSQL database, or any otherformat or structure for use as a datastore that a person skilled in theart would recognize as suitable upon review of the entirety of thisdisclosure. Aircraft marker database 1012 may include a plurality ofdata entries and/or records corresponding to aircraft types as describedabove. Data entries and/or records may describe, without limitation,data concerning light sport data, private pilot data, powered lift data,multi-engine data, high performance data, and rotary wing data. Dataentries in an aircraft marker database 1012 may be flagged with orlinked to one or more additional elements of information, which may bereflected in data entry cells and/or in linked tables such as tablesrelated by one or more indices in a relational database; one or moreadditional elements of information may include data associating a flightclassification and/or permission with one or more cohorts, includingaircraft type groupings, such as fixed conventional, fixed wing complex,light sport, private pilot, instrument, complex, multi-engine, highperformance, tail wheel, sea plane, rotorcraft, powered lift,commercial, VTOL, eVTOL, or the like. Additional elements of informationmay include one or more categories of pilot license data as describedabove. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which data entries in anaircraft marker database 1012 may reflect categories, cohorts, and/orpopulations of data consistently with this disclosure.

Still referring to FIG. 11 , one or more database tables in aircraftmarker database 1012 may include, as a non-limiting example, light sportdata table 1100. Light sport data table 1100 may be a table matchingpilot restriction received from a device, such as user device 148, asdescribed above, to a user with an endorsement data of a light sportpilot of the aircraft marker. For instance, and without limitation,aircraft marker database 1012 may include a light sport data table 1100listing aircraft able to be piloted by a user with light sportendorsement data, such as Czech Sport Aircraft Sportcruiser, Icon A5,Cessna Skycatcher, Tecnam Turbo P2008, Tecnam Turbo Astore, Rans S-7LSCourier, Colyaer Freedom 5100, 3XTrim 3XLS Navigator 600, Aerospool WT-9Dynamic, American Legend AL3C-100, BRM Aero Bristell LSA, Cessna 162,Czech Sport Aircraft Pipersport, Ekolot KR030 Topaz, and the like, asdescribed above in further detail in reference to FIG. 10 .

With continued reference to FIG. 11 , aircraft marker database 1012 mayinclude, as a non-limiting example, a private pilot data table 1104. Forinstance, and without limitation, aircraft marker database 1012 mayinclude private pilot data table 1104 matching/correlating pilotrestriction data received from a device, such as user device 148, asdescribed above, to a user with an endorsement data of a private pilotof the aircraft marker. For instance and without limitation, aircraftmarker database 1012 may include private pilot data table 1104 listingaircraft able to be piloted by a user with a private pilot endorsementdatum, such as an aircraft having no more than 180 horsepower, such assingle-engine airplanes, multi-engine airplanes, rotorcraft, gliders,powered-lift, and the like, as described above in further detail inreference to FIG. 10 . As another non-limiting example, aircraft markerdatabase 1012 may include, as a non-limiting example, a powered liftdata table 1108. For instance, and without limitation, aircraft markerdatabase 1012 may include powered lift data table 1108 matching pilotrestriction received from a device, such as user device 148, asdescribed above, to a user with an endorsement data of powered lift ofthe aircraft marker. For instance and without limitation, aircraftmarker database 1012 may include powered lift data table 1108 listingaircraft able to be piloted by a user with a powered lift endorsementdata, such as a heavier-than-air aircraft capable of vertical takeoff,vertical landing, and low speed flight that depends principally onengine-driven lift devices or engine thrust for lift during these flightregimes and on nonrotating airfoil(s) for lift during horizontal flight,such as a convertiplane, tiltrotor aircraft, tiltwing aircraft,tail-sitter aircraft, vectored thrust aircraft, lift jet aircraft, liftfan aircraft, lift via Coandă effect, eVTOL aircraft, and the like, asdescribed above in further detail in reference to FIG. 10 .

Still referring to FIG. 11 , aircraft marker database 1012 may include,as a non-limiting example, a multi-engine data table 1148. For instance,and without limitation, aircraft marker database 1012 may includemulti-engine data table 1148 matching pilot restriction received from adevice, such as user device 148, as described above, to a user with anendorsement data of multi-engine of the aircraft marker. For instanceand without limitation, aircraft marker database 1012 may includemulti-engine data table 1148 listing aircraft able to be piloted by auser with multi-engine endorsement data, such as an aircraft having morethan one engine, such as Beechcraft 58 Baron, Vulcanair P-68, DiamondDA-62, Piper Seneca III, Piper Aztec, Cessna 310, Cessna 340, Cessna412, Beechcraft B60 Duke, Cessna T303 Crusader, Piper Navajo Chieftain,Boeing 747, Airbus A340, and the like, as described above in furtherdetail in reference to FIG. 10 . As a further non-limiting example,aircraft marker database 1012 may include a rotary wing data table 1116.For instance, and without limitation, aircraft marker database 1012 mayinclude rotary wing data table 1116 matching pilot restriction receivedfrom a device, such as user device 148, as described above, to a userwith an endorsement data of rotary wing of the aircraft marker. Forinstance and without limitation, aircraft marker database 1012 mayinclude rotary wing data table 1116 listing aircraft able to be pilotedby a user with rotary wing endorsement data, such as a helicopter, suchas Robinson R22, Eurocopter AS350 Ecureuil, Sikorsky UH-60 Black Hawk,Highes OH-6 Cayuse, Robinson R44, MIL MI-2, Bell 47, Bell 406 Jetranger,Bell 222, Augusta Westland 109 Power Grand, Augusta Westland 139,Eurocopter 120 Colibri, McDonnell Douglas MD 900, Sikorsky S-76, and thelike, as described above in further detail in reference to FIG. 10 .Tables presented above are presented for exemplary purposes only;persons skilled in the art will be aware of various ways in which datamay be organized in aircraft marker database 1012 consistently with thisdisclosure.

Referring now to FIG. 12 , an embodiment of pilot log database 716 isillustrated. Pilot log database 716 may include any data structure forordered storage and retrieval of data, which may be implemented as ahardware or software module. Pilot log database 716 may be implemented,without limitation, as a relational database, a key-value retrievaldatastore such as a NOSQL database, or any other format or structure foruse as a datastore that a person skilled in the art would recognize assuitable upon review of the entirety of this disclosure. Pilot logdatabase 716 may include a plurality of data entries and/or recordscorresponding to elements as described above. Data entries and/orrecords may describe, without limitation, data concerning the pluralityof maneuver data 800, element of flight mapping datum, and the timetablerestriction.

Still referring to FIG. 12 , one or more database tables in pilot logdatabase 716 may include, as a non-limiting example, a maneuver datatable 1200. Maneuver data table 1200 may be a table storing eachmaneuver data of the plurality of maneuver data 800 generated byregistry module 136. For instance, and without limitation, pilot logdatabase 716 may include maneuver data table 1200 listing each maneuverdata of the plurality of maneuver data 800, the associated data of eachmaneuver data, such as each tactical movement data of the plurality oftactical movement data 804, each correlated element of flight movementdata, and the like.

Continuing to refer to FIG. 12 , one or more database tables in pilotlog database 716 may include, as a non-limiting example, a flightmapping datum table 1204. Flight mapping datum table 1204 may be a tablestoring the flight mapping datum received by the tracking unit 816disposed on electric aircraft 152. For instance, and without limitation,pilot log database 716 may include a flight mapping datum table 1204listing the flight mapping datum received by the tracking unit 816disposed on electric aircraft 152, such as the latitude and longitude ofthe electric aircraft continuously measured during the duration of aflight.

With continued reference to FIG. 12 , one or more database tables inpilot log database 716 may include, as a non-limiting example, atimetable restriction datum table 1208. Timetable restriction data table1208 may be a table storing the timetable restriction datum received bythe user device 148 and/or instructor device 420. For instance, andwithout limitation, pilot log database 716 may include a timetablerestriction datum table 1208 listing timetable restriction datumreceived by the user device 148, such as the availability, in time ofday, of the pilot to participate in a flight. Tables presented above arepresented for exemplary purposes only; persons skilled in the art willbe aware of various ways in which data may be organized in pilot logdatabase 716 consistently with this disclosure.

Referring now to FIG. 13 , an embodiment of unsupervisedmachine-learning model 1300 is illustrated. Unsupervised learning mayinclude any of the unsupervised learning processes as described herein.Unsupervised machine-learning model 1300 includes any clusteringunsupervised machine-learning model as described herein. Unsupervisedmachine-learning model 1300 generates at least a second correlatedaircraft performance model output 1324. The at least a second correlatedaircraft performance model output 1324 is generated as a function of theplurality of measured aircraft operation datum and the correlateddataset. Correlated dataset may be selected from a schedule database 200or any database as described herein. Schedule database 200 may containdata describing different characteristics of the plurality of measuredaircraft operation datum 108, wherein the plurality of measured aircraftoperation datum is described herein, which may be organized intocategories contained within a schedule database 200 as described herein.Unsupervised machine-learning model may further include a hierarchicalclustering model 1304. Hierarchical clustering model 1304 may groupand/or segment datasets into hierarchy clusters including bothagglomerative and divisive clusters. Agglomerative clusters may includea bottom up approach where each observation starts in its own clusterand pairs of clusters are merged as one moves up the hierarchy. Divisiveclusters may include a top down approach where all observations maystart in one cluster and splits are performed recursively as one movesdown the hierarchy. In an embodiment, hierarchical clustering model 1304may analyze datasets obtained from schedule database 200 to findobservations which may each initially form own cluster. Hierarchicalclustering model 1304 may then then identify clusters that are closesttogether and merge the two most similar clusters and continue until allclusters are merged together. Hierarchical clustering model 1304 mayoutput a dendrogram which may describe the hierarchical relationshipbetween the clusters. Distance between clusters that are created may bemeasured using a suitable metric. Distance may be measured between forexample the two most similar parts of a cluster known as single linkage,the two least similar bits of a cluster known as complete-linkage, thecenter of the clusters known as average-linkage or by some othercriterion which may be obtained based on input received from anydatabase as described herein, as an example.

With continued reference to FIG. 13 , unsupervised machine-learningmodel 1300 may perform other unsupervised machine learning models tooutput at least an aircraft operation model output 1324. Unsupervisedmachine-learning model 1300 may include a data clustering model 1308.Data clustering model 1308 may group and/or segment datasets with sharedattributes to extrapolate algorithmic relationships. Data clusteringmodel 1308 may group data that has been labelled, classified, and/orcategorized. Data clustering model 1308 may identify commonalities indata and react based on the presence or absence of such commonalities.For instance and without limitation, data clustering model 1308 mayidentify other data sets that contain the same or similarcharacteristics of the measured aircraft operation datum containedwithin plurality of measured aircraft operation datum 1308 or identifyother datasets that contain parts with similar attributes and/ordifferentiations. In an embodiment, data clustering model 1308 maycluster data and generate labels that may be utilized as training setdata. Data clustering model 1308 may utilize other forms of dataclustering algorithms including for example, hierarchical clustering,k-means, mixture models, OPTICS algorithm, and DBSCAN.

With continued reference to FIG. 13 , unsupervised machine-learningmodel 1300 may include an anomaly detection model 1312, Anomalydetection model 1312 may include identification of rare items, events orobservations that differ significant from the majority of the data.Anomaly detection model 1312 may function to observe and find outliers.For instance and without limitation, anomaly detect may find and examinedata outliers such as a that is not compatible with any part elements orthat is compatible with very few part elements.

Still referring to FIG. 13 , unsupervised machine-learning model 1300may include other unsupervised machine-learning models 1316. This mayinclude for example, neural networks, autoencoders, deep belief nets,Hebbian learning, adversarial networks, self-organizing maps,expectation-maximization algorithm, method of moments, blind signalseparation techniques, principal component analysis, independentcomponent analysis, non-negative matrix factorization, singular valuedecomposition.

Referring now to FIG. 14 , an embodiment of supervised machine learningmodel 14160 is illustrated. Supervised machine-learning model 14160 isconfigured to generate an aircraft performance model output 1412.Aircraft performance model output 1412 is generated as a function ofrelating plurality of measured aircraft operation datum to at least anaircraft performance model output. Supervised machine-learning model14160 generates the aircraft performance model output 1412 using firsttraining set 14168. Supervised machine-learning model 14160 may beconfigured to perform any supervised machine-learning algorithm asdescribed above herein. This may include for example, support vectormachines, linear regression, logistic regression, naïve Bayes, lineardiscriminant analysis, decision trees, k-nearest neighbor algorithm,neural networks, and similarity learning. In an embodiment, firsttraining set 14168 may include the at least a correlated dataset.Supervised machine-learning model 14160 may be further configured tocalculate the external milling time as a function of relating theplurality of measured aircraft operation datum to the aircraftperformance model output 1412.

With continued reference to FIG. 14 , supervised learning model 14160may include a graphic processing unit (GPU) 1416. As described herein,GPU 112 may include a device with a set of specific hardwarecapabilities that are intended to map well to the way that various 3Dengines execute their code, including geometry setup and execution,texture mapping, memory access, and shaders. GPU 1416 may include,without limitation, a specialized electronic circuit designed to rapidlymanipulate and alter memory to accelerate the creation of images in aframe buffer. For instance, and without limitation, GPU 1416 may includea computer chip that performs rapid mathematical calculations, primarilyfor the purpose of rendering images. GPU 1416 may further include,without limitation, full scene anti-aliasing (FSAA) to smooth the edgesof 3-D objects and anisotropic filtering (AF) to make images lookcrisper. GPU 1416 may include, without limitation, dedicated graphicscards, integrated graphics cards, hybrid graphics cards, and/or anycombination thereof. GPU 1416 may be configured to calculate the volumeremoved by each tool of the plurality of tools for the at least acorrelated compatible part element as a function of the at least aninternal request datum.

Continuing to refer to FIG. 14 , supervised machine-learning model 14160may generate aircraft performance model output 1412 by executing a lazylearning module 14164. Lazy learning module 14164 is executed as afunction of manufacturing request datum and the at least a part element.A lazy-learning process and/or protocol, which may alternatively bereferred to as a “lazy loading” or “call-when-needed” process and/orprotocol, may be a process whereby machine learning is conducted uponreceipt of an input to be converted to an output, by combining the inputand training set to derive the algorithm to be used to produce theoutput on demand. For instance, an initial set of simulations may beperformed to cover a “first guess” at a data associated with at least ameasured aircraft operation datum, using at least a training set.Aircraft performance model output such as a virtual representation orperformance alert that may include a plurality of time reliant schedulesmay be calculated using the following equation:

$T^{MKT} = {\frac{V_{1}}{{MRR}_{1}} + \frac{V_{2}}{{MRR}_{2}} + \frac{V_{3}}{{MRR}_{3}} + {\ldots\frac{V_{n}}{{MRR}_{n}}} + \frac{S_{1}}{{ARR}_{1}} + \frac{S_{2}}{{ARR}_{2}} + \frac{S_{3}}{{ARR}_{3}} + {\ldots\frac{S_{n}}{{ARR}_{n}}}}$

where T^(MKT) is the external milling time, V_(n) is the volume removedby each tool of the plurality of tools, MRR_(n) is the material removalrate of each tool of the plurality of tools, S_(n) is the surface arearemoved by each tool of the plurality of tools, and ARR_(n) is the arearemoval rate of each tool of the plurality of tools. Heuristic mayinclude calculating external price output according to associationsand/or compatible part elements. External price output may be calculatedusing the following equation:P ^(MKT)=(T ^(MKT)·<Mill Rate>+<Material cost>+<Labor cost>+<Overheadcost>+<Rework cost>+<Operating costs>)·<Mark up>  [EQ 02]where P^(MKT) is the aircraft performance model output, and T^(MKT) isthe time reliant schedule as calculated above. Lazy learning module14164 may alternatively or additionally implement any suitable “lazylearning” algorithm, including without limitation a K-nearest neighborsalgorithm, a lazy naïve Bayes algorithm, or the like; persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various lazy-learning algorithms that may be applied to generateexternal price outputs as described in this disclosure, includingwithout limitation lazy learning applications of machine-learningalgorithms as described in further detail below.

Referring now to FIG. 15 , a flow diagram of a method 1500 for acomputing device configured for use for fleet management is presented.Method 1500 includes step 1505 which includes receiving, by a computingdevice communicatively connected to at least an electric aircraft, aplurality of measured aircraft operation datum from a sensor disposed onthe at least an electric aircraft. Receiving the plurality of measuredaircraft operation datum may include a sensor configured to detect aplurality of measured aircraft operation datum including aircraftcomponent state data, a payload data, and a pilot data.

With continued reference to FIG. 15 , method 1500 includes step 1510which includes selecting a training set as a function of each measuredaircraft operation datum of the plurality of measured aircraft operationdatum and the at least an electric aircraft, wherein each measuredaircraft operation datum of the plurality of measured aircraft operationdatum is correlated to an element of modeled aircraft data. Selecting atraining set may be include correlating a plurality of measured aircraftoperation datum to an element of modeled aircraft data. The element ofmodeled aircraft data may include a simulation of a flight. Selectingthe training data may be performed utilizing any means of selection asdescribed in the entirety of this disclosure. In a non-limitingembodiment, selecting a training set may include correlating theplurality of measured aircraft operation datum to an aircraftperformance model output 136 which may include a health projection forat least a flight component associated.

With continued reference to FIG. 15 , method 1500 includes step 1515which includes generating, using a machine-learning algorithm, anaircraft performance model output based on the plurality of measuredaircraft operation datum and the selected training set. Generating theaircraft performance model output may be performed using a supervisedmachine-learning model. Generating the aircraft performance model outputmay be performed using an unsupervised machine-learning model.Generating the aircraft performance model output may include generatinga plurality of information in various forms. In a non-limitingembodiment, step 1515 may include generating a flight simulation, ahealth projection, a health history, a plurality of schedules, and thelike thereof.

With continued reference to FIG. 15 , method 1500 includes step 1420which includes generating a performance alert. Performance alert mayinclude any performance alert as described in the entirety of thisdisclosure. In a non-limiting embodiment, a flight controller may beconfigured to record a plurality of data during an occurrence of anissue or during an occurrence of a predictive issue that may occur. In anon-limiting embodiment, performance alert may include a plurality ofperformance alerts for each major flight component. For instance, aperformance alert may flash and alert a user during an occurrence of afailure or degradation of a flight component during flight, beforetakeoff, after landing, and the like thereof. In a non-limitingembodiment, performance alert 146 be triggered when an obstructiveoutside parameter is detected. A person of ordinary skill in the art,after viewing the entirety of this disclosure, would appreciate thefunction of an alert in the context of detecting, avoiding, andanalyzing unexpected and potentially hazardous obstacles and/or issues.

Referring now to FIG. 16 , an exemplary embodiment of an aircraft 1600,which may include, or be incorporated with, a system for optimization ofa recharging flight plan is illustrated. As used in this disclosure an“aircraft” is any vehicle that may fly by gaining support from the air.As a non-limiting example, aircraft may include airplanes, helicopters,commercial and/or recreational aircraft, instrument flight aircraft,drones, electric aircraft, airliners, rotorcrafts, vertical takeoff andlanding aircraft, jets, airships, blimps, gliders, paramotors, and thelike thereof.

Still referring to FIG. 16 , aircraft 1600 may include an electricallypowered aircraft. In embodiments, electrically powered aircraft may bean electric vertical takeoff and landing (eVTOL) aircraft. Aircraft 1600may include an unmanned aerial vehicle and/or a drone. Electric aircraftmay be capable of rotor-based cruising flight, rotor-based takeoff,rotor-based landing, fixed-wing cruising flight, airplane-style takeoff,airplane-style landing, and/or any combination thereof. Electricaircraft may include one or more manned and/or unmanned aircraft.Electric aircraft may include one or more all-electric short takeoff andlanding (eSTOL) aircraft. For example, and without limitation, eSTOLaircraft may accelerate the plane to a flight speed on takeoff anddecelerate the plane after landing. In an embodiment, and withoutlimitation, electric aircraft may be configured with an electricpropulsion assembly. Electric propulsion assembly may include anyelectric propulsion assembly as described in U.S. Nonprovisionalapplication Ser. No. 16/703,225, filed on Dec. 4, 2019, and entitled “ANINTEGRATED ELECTRIC PROPULSION ASSEMBLY,” the entirety of which isincorporated herein by reference. For purposes of description herein,the terms “upper,” “lower,” “left,” “rear,” “right,” “front,”“vertical,” “horizontal,” “upward,” “downward,” “forward,” “backward”and derivatives thereof shall relate to the invention as oriented inFIG. 16 .

Still referring to FIG. 16 , aircraft 1600 includes a fuselage 1604. Asused in this disclosure a “fuselage” is the main body of an aircraft, orin other words, the entirety of the aircraft except for the cockpit,nose, wings, empennage, nacelles, any and all control surfaces, andgenerally contains an aircraft's payload. Fuselage 1604 may includestructural elements that physically support a shape and structure of anaircraft. Structural elements may take a plurality of forms, alone or incombination with other types. Structural elements may vary depending ona construction type of aircraft such as without limitation a fuselage1604. Fuselage 1604 may comprise a truss structure. A truss structuremay be used with a lightweight aircraft and comprises welded steel tubetrusses. A “truss,” as used in this disclosure, is an assembly of beamsthat create a rigid structure, often in combinations of triangles tocreate three-dimensional shapes. A truss structure may alternativelycomprise wood construction in place of steel tubes, or a combinationthereof. In embodiments, structural elements may comprise steel tubesand/or wood beams. In an embodiment, and without limitation, structuralelements may include an aircraft skin. Aircraft skin may be layered overthe body shape constructed by trusses. Aircraft skin may comprise aplurality of materials such as plywood sheets, aluminum, fiberglass,and/or carbon fiber, the latter of which will be addressed in greaterdetail later herein.

In embodiments, and with continued reference to FIG. 16 , aircraftfuselage 1604 may include and/or be constructed using geodesicconstruction. Geodesic structural elements may include stringers woundabout formers (which may be alternatively called station frames) inopposing spiral directions. A “stringer,” as used in this disclosure, isa general structural element that includes a long, thin, and rigid stripof metal or wood that is mechanically coupled to and spans a distancefrom, station frame to station frame to create an internal skeleton onwhich to mechanically couple aircraft skin. A former (or station frame)may include a rigid structural element that is disposed along a lengthof an interior of aircraft fuselage 1604 orthogonal to a longitudinal(nose to tail) axis of the aircraft and may form a general shape offuselage 1604. A former may include differing cross-sectional shapes atdiffering locations along fuselage 1604, as the former is the structuralelement that informs the overall shape of a fuselage 1604 curvature. Inembodiments, aircraft skin may be anchored to formers and strings suchthat the outer mold line of a volume encapsulated by formers andstringers comprises the same shape as aircraft 1600 when installed. Inother words, former(s) may form a fuselage's ribs, and the stringers mayform the interstitials between such ribs. The spiral orientation ofstringers about formers may provide uniform robustness at any point onan aircraft fuselage such that if a portion sustains damage, anotherportion may remain largely unaffected. Aircraft skin may be mechanicallycoupled to underlying stringers and formers and may interact with afluid, such as air, to generate lift and perform maneuvers.

In an embodiment, and still referring to FIG. 16 , fuselage 1604 mayinclude and/or be constructed using monocoque construction. Monocoqueconstruction may include a primary structure that forms a shell (or skinin an aircraft's case) and supports physical loads. Monocoque fuselagesare fuselages in which the aircraft skin or shell is also the primarystructure. In monocoque construction aircraft skin would support tensileand compressive loads within itself and true monocoque aircraft can befurther characterized by the absence of internal structural elements.Aircraft skin in this construction method is rigid and can sustain itsshape with no structural assistance form underlying skeleton-likeelements. Monocoque fuselage may comprise aircraft skin made fromplywood layered in varying grain directions, epoxy-impregnatedfiberglass, carbon fiber, or any combination thereof.

According to embodiments, and further referring to FIG. 16 , fuselage1604 may include a semi-monocoque construction. Semi-monocoqueconstruction, as used herein, is a partial monocoque construction,wherein a monocoque construction is describe above detail. Insemi-monocoque construction, aircraft fuselage 1604 may derive somestructural support from stressed aircraft skin and some structuralsupport from underlying frame structure made of structural elements.Formers or station frames can be seen running transverse to the longaxis of fuselage 1604 with circular cutouts which are generally used inreal-world manufacturing for weight savings and for the routing ofelectrical harnesses and other modern on-board systems. In asemi-monocoque construction, stringers are thin, long strips of materialthat run parallel to fuselage's long axis. Stringers may be mechanicallycoupled to formers permanently, such as with rivets. Aircraft skin maybe mechanically coupled to stringers and formers permanently, such as byrivets as well. A person of ordinary skill in the art will appreciate,upon reviewing the entirety of this disclosure, that there are numerousmethods for mechanical fastening of the aforementioned components likescrews, nails, dowels, pins, anchors, adhesives like glue or epoxy, orbolts and nuts, to name a few. A subset of fuselage under the umbrellaof semi-monocoque construction includes unibody vehicles. Unibody, whichis short for “unitized body” or alternatively “unitary construction,”vehicles are characterized by a construction in which the body, floorplan, and chassis form a single structure. In the aircraft world,unibody may be characterized by internal structural elements likeformers and stringers being constructed in one piece, integral to theaircraft skin as well as any floor construction like a deck.

Still referring to FIG. 16 , stringers and formers, which may accountfor the bulk of an aircraft structure excluding monocoque construction,may be arranged in a plurality of orientations depending on aircraftoperation and materials. Stringers may be arranged to carry axial(tensile or compressive), shear, bending or torsion forces throughouttheir overall structure. Due to their coupling to aircraft skin,aerodynamic forces exerted on aircraft skin will be transferred tostringers. A location of said stringers greatly informs the type offorces and loads applied to each and every stringer, all of which may behandled by material selection, cross-sectional area, and mechanicalcoupling methods of each member. A similar assessment may be made forformers. In general, formers may be significantly larger incross-sectional area and thickness, depending on location, thanstringers. Both stringers and formers may comprise aluminum, aluminumalloys, graphite epoxy composite, steel alloys, titanium, or anundisclosed material alone or in combination.

In an embodiment, and still referring to FIG. 16 , stressed skin, whenused in semi-monocoque construction is the concept where the skin of anaircraft bears partial, yet significant, load in an overall structuralhierarchy. In other words, an internal structure, whether it be a frameof welded tubes, formers and stringers, or some combination, may not besufficiently strong enough by design to bear all loads. The concept ofstressed skin may be applied in monocoque and semi-monocoqueconstruction methods of fuselage 1604. Monocoque comprises onlystructural skin, and in that sense, aircraft skin undergoes stress byapplied aerodynamic fluids imparted by the fluid. Stress as used incontinuum mechanics may be described in pound-force per square inch(lbf/in²) or Pascals (Pa). In semi-monocoque construction stressed skinmay bear part of aerodynamic loads and additionally may impart force onan underlying structure of stringers and formers.

Still referring to FIG. 16 , it should be noted that an illustrativeembodiment is presented only, and this disclosure in no way limits theform or construction method of a system and method for loading payloadinto an eVTOL aircraft. In embodiments, fuselage 1604 may beconfigurable based on the needs of the eVTOL per specific mission orobjective. The general arrangement of components, structural elements,and hardware associated with storing and/or moving a payload may beadded or removed from fuselage 1604 as needed, whether it is stowedmanually, automatedly, or removed by personnel altogether. Fuselage 1604may be configurable for a plurality of storage options. Bulkheads anddividers may be installed and uninstalled as needed, as well aslongitudinal dividers where necessary. Bulkheads and dividers may beinstalled using integrated slots and hooks, tabs, boss and channel, orhardware like bolts, nuts, screws, nails, clips, pins, and/or dowels, toname a few. Fuselage 1604 may also be configurable to accept certainspecific cargo containers, or a receptable that can, in turn, acceptcertain cargo containers.

Still referring to FIG. 16 , aircraft 1600 may include a plurality oflaterally extending elements attached to fuselage 1604. As used in thisdisclosure a “laterally extending element” is an element that projectsessentially horizontally from fuselage, including an outrigger, a spar,and/or a fixed wing that extends from fuselage. Wings may be structureswhich include airfoils configured to create a pressure differentialresulting in lift. Wings may generally dispose on the left and rightsides of the aircraft symmetrically, at a point between nose andempennage. Wings may comprise a plurality of geometries in planformview, swept swing, tapered, variable wing, triangular, oblong,elliptical, square, among others. A wing's cross section geometry maycomprise an airfoil. An “airfoil” as used in this disclosure is a shapespecifically designed such that a fluid flowing above and below it exertdiffering levels of pressure against the top and bottom surface. Inembodiments, the bottom surface of an aircraft can be configured togenerate a greater pressure than does the top, resulting in lift.Laterally extending element may comprise differing and/or similarcross-sectional geometries over its cord length or the length from wingtip to where wing meets the aircraft's body. One or more wings may besymmetrical about the aircraft's longitudinal plane, which comprises thelongitudinal or roll axis reaching down the center of the aircraftthrough the nose and empennage, and the plane's yaw axis. Laterallyextending element may comprise controls surfaces configured to becommanded by a pilot or pilots to change a wing's geometry and thereforeits interaction with a fluid medium, like air. Control surfaces maycomprise flaps, ailerons, tabs, spoilers, and slats, among others. Thecontrol surfaces may dispose on the wings in a plurality of locationsand arrangements and in embodiments may be disposed at the leading andtrailing edges of the wings, and may be configured to deflect up, down,forward, aft, or a combination thereof. An aircraft, including adual-mode aircraft may comprise a combination of control surfaces toperform maneuvers while flying or on ground.

Still referring to FIG. 16 , aircraft 1600 includes a plurality offlight components 1608. As used in this disclosure a “flight component”is a component that promotes flight and guidance of an aircraft. In anembodiment, flight component 1608 may be mechanically coupled to anaircraft. As used herein, a person of ordinary skill in the art wouldunderstand “mechanically coupled” to mean that at least a portion of adevice, component, or circuit is connected to at least a portion of theaircraft via a mechanical coupling. Said mechanical coupling caninclude, for example, rigid coupling, such as beam coupling, bellowscoupling, bushed pin coupling, constant velocity, split-muff coupling,diaphragm coupling, disc coupling, donut coupling, elastic coupling,flexible coupling, fluid coupling, gear coupling, grid coupling, hirthjoints, hydrodynamic coupling, jaw coupling, magnetic coupling, Oldhamcoupling, sleeve coupling, tapered shaft lock, twin spring coupling, ragjoint coupling, universal joints, or any combination thereof. In anembodiment, mechanical coupling may be used to connect the ends ofadjacent parts and/or objects of an electric aircraft. Further, in anembodiment, mechanical coupling may be used to join two pieces ofrotating electric aircraft components.

Still referring to FIG. 16 , plurality of flight components 1608 mayinclude at least a lift propulsor component 1612. As used in thisdisclosure a “lift propulsor component” is a component and/or deviceused to propel a craft upward by exerting downward force on a fluidmedium, which may include a gaseous medium such as air or a liquidmedium such as water. Lift propulsor component 1612 may include anydevice or component that consumes electrical power on demand to propelan electric aircraft in a direction or other vehicle while on ground orin-flight. For example, and without limitation, lift propulsor component1612 may include a rotor, propeller, paddle wheel and the like thereof,wherein a rotor is a component that produces torque along thelongitudinal axis, and a propeller produces torquer along the verticalaxis. In an embodiment, lift propulsor component 1612 includes aplurality of blades. As used in this disclosure a “blade” is a propellerthat converts rotary motion from an engine or other power source into aswirling slipstream. In an embodiment, blade may convert rotary motionto push the propeller forwards or backwards. In an embodiment liftpropulsor component 1612 may include a rotating power-driven hub, towhich are attached several radial airfoil-section blades such that thewhole assembly rotates about a longitudinal axis. Blades may beconfigured at an angle of attack, wherein an angle of attack isdescribed in detail below. In an embodiment, and without limitation,angle of attack may include a fixed angle of attack. As used in thisdisclosure a “fixed angle of attack” is fixed angle between a chord lineof a blade and relative wind. As used in this disclosure a “fixed angle”is an angle that is secured and/or unmovable from the attachment point.For example, and without limitation fixed angle of attack may be 3.2° asa function of a pitch angle of 19.7° and a relative wind angle 16.5°. Inanother embodiment, and without limitation, angle of attack may includea variable angle of attack. As used in this disclosure a “variable angleof attack” is a variable and/or moveable angle between a chord line of ablade and relative wind. As used in this disclosure a “variable angle”is an angle that is moveable from an attachment point. For example, andwithout limitation variable angle of attack may be a first angle of 4.7°as a function of a pitch angle of 17.1° and a relative wind angle 16.4°,wherein the angle adjusts and/or shifts to a second angle of 16.7° as afunction of a pitch angle of 16.1° and a relative wind angle 16.4°. Inan embodiment, angle of attack be configured to produce a fixed pitchangle. As used in this disclosure a “fixed pitch angle” is a fixed anglebetween a cord line of a blade and the rotational velocity direction.For example, and without limitation, fixed pitch angle may include 18°.In another embodiment fixed angle of attack may be manually variable toa few set positions to adjust one or more lifts of the aircraft prior toflight. In an embodiment, blades for an aircraft are designed to befixed to their hub at an angle similar to the thread on a screw makes anangle to the shaft; this angle may be referred to as a pitch or pitchangle which will determine a speed of forward movement as the bladerotates.

In an embodiment, and still referring to FIG. 16 , lift propulsorcomponent 1612 may be configured to produce a lift. As used in thisdisclosure a “lift” is a perpendicular force to the oncoming flowdirection of fluid surrounding the surface. For example, and withoutlimitation relative air speed may be horizontal to aircraft 1600,wherein lift force may be a force exerted in a vertical direction,directing aircraft 1600 upwards. In an embodiment, and withoutlimitation, lift propulsor component 1612 may produce lift as a functionof applying a torque to lift propulsor component. As used in thisdisclosure a “torque” is a measure of force that causes an object torotate about an axis in a direction. For example, and withoutlimitation, torque may rotate an aileron and/or rudder to generate aforce that may adjust and/or affect altitude, airspeed velocity,groundspeed velocity, direction during flight, and/or thrust. Forexample, one or more flight components such as a power sources may applya torque on lift propulsor component 1612 to produce lift. As used inthis disclosure a “power source” is a source that that drives and/orcontrols any other flight component. For example, and without limitationpower source may include a motor that operates to move one or more liftpropulsor components, to drive one or more blades, or the like thereof.A motor may be driven by direct current (DC) electric power and mayinclude, without limitation, brushless DC electric motors, switchedreluctance motors, induction motors, or any combination thereof. A motormay also include electronic speed controllers or other components forregulating motor speed, rotation direction, and/or dynamic braking.

Still referring to FIG. 16 , power source may include an energy source.An energy source may include, for example, an electrical energy source agenerator, a photovoltaic device, a fuel cell such as a hydrogen fuelcell, direct methanol fuel cell, and/or solid oxide fuel cell, anelectric energy storage device (e.g., a capacitor, an inductor, and/or abattery). An electrical energy source may also include a battery cell,or a plurality of battery cells connected in series into a module andeach module connected in series or in parallel with other modules.Configuration of an energy source containing connected modules may bedesigned to meet an energy or power requirement and may be designed tofit within a designated footprint in an electric aircraft in whichaircraft 1600 may be incorporated.

In an embodiment, and still referring to FIG. 16 , an energy source maybe used to provide a steady supply of electrical power to a load overthe course of a flight by a vehicle or other electric aircraft. Forexample, an energy source may be capable of providing sufficient powerfor “cruising” and other relatively low-energy phases of flight. Anenergy source may also be capable of providing electrical power for somehigher-power phases of flight as well, particularly when the energysource is at a high SOC, as may be the case for instance during takeoff.In an embodiment, an energy source may be capable of providingsufficient electrical power for auxiliary loads including withoutlimitation, lighting, navigation, communications, de-icing, steering orother systems requiring power or energy. Further, an energy source maybe capable of providing sufficient power for controlled descent andlanding protocols, including, without limitation, hovering descent orrunway landing. As used herein an energy source may have high powerdensity where electrical power an energy source can usefully produce perunit of volume and/or mass is relatively high. “Electrical power,” asused in this disclosure, is defined as a rate of electrical energy perunit time. An energy source may include a device for which power thatmay be produced per unit of volume and/or mass has been optimized, atthe expense of the maximal total specific energy density or powercapacity, during design. Non-limiting examples of items that may be usedas at least an energy source may include batteries used for startingapplications including Li ion batteries which may include NCA, NMC,Lithium iron phosphate (LiFePO4) and Lithium Manganese Oxide (LMO)batteries, which may be mixed with another cathode chemistry to providemore specific power if the application requires Li metal batteries,which have a lithium metal anode that provides high power on demand, Liion batteries that have a silicon or titanite anode, energy source maybe used, in an embodiment, to provide electrical power to an electricaircraft or drone, such as an electric aircraft vehicle, during momentsrequiring high rates of power output, including without limitationtakeoff, landing, thermal de-icing and situations requiring greaterpower output for reasons of stability, such as high turbulencesituations, as described in further detail below. A battery may include,without limitation a battery using nickel based chemistries such asnickel cadmium or nickel metal hydride, a battery using lithium ionbattery chemistries such as a nickel cobalt aluminum (NCA), nickelmanganese cobalt (NMC), lithium iron phosphate (LiFePO4), lithium cobaltoxide (LCO), and/or lithium manganese oxide (LMO), a battery usinglithium polymer technology, lead-based batteries such as withoutlimitation lead acid batteries, metal-air batteries, or any othersuitable battery. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various devices ofcomponents that may be used as an energy source.

Still referring to FIG. 16 , an energy source may include a plurality ofenergy sources, referred to herein as a module of energy sources. Amodule may include batteries connected in parallel or in series or aplurality of modules connected either in series or in parallel designedto deliver both the power and energy requirements of the application.Connecting batteries in series may increase the voltage of at least anenergy source which may provide more power on demand. High voltagebatteries may require cell matching when high peak load is needed. Asmore cells are connected in strings, there may exist the possibility ofone cell failing which may increase resistance in the module and reducean overall power output as a voltage of the module may decrease as aresult of that failing cell. Connecting batteries in parallel mayincrease total current capacity by decreasing total resistance, and italso may increase overall amp-hour capacity. Overall energy and poweroutputs of at least an energy source may be based on individual batterycell performance or an extrapolation based on measurement of at least anelectrical parameter. In an embodiment where an energy source includes aplurality of battery cells, overall power output capacity may bedependent on electrical parameters of each individual cell. If one cellexperiences high self-discharge during demand, power drawn from at leastan energy source may be decreased to avoid damage to the weakest cell.An energy source may further include, without limitation, wiring,conduit, housing, cooling system and battery management system. Personsskilled in the art will be aware, after reviewing the entirety of thisdisclosure, of many different components of an energy source.

In an embodiment and still referring to FIG. 16 , plurality of flightcomponents 1608 may be arranged in a quad copter orientation. As used inthis disclosure a “quad copter orientation” is at least a lift propulsorcomponent oriented in a geometric shape and/or pattern, wherein each ofthe lift propulsor components are located along a vertex of thegeometric shape. For example, and without limitation, a square quadcopter orientation may have four lift propulsor components oriented inthe geometric shape of a square, wherein each of the four lift propulsorcomponents are located along the four vertices of the square shape. As afurther non-limiting example, a hexagonal quad copter orientation mayhave six lift propulsor components oriented in the geometric shape of ahexagon, wherein each of the six lift propulsor components are locatedalong the six vertices of the hexagon shape. In an embodiment, andwithout limitation, quad copter orientation may include a first set oflift propulsor components and a second set of lift propulsor components,wherein the first set of lift propulsor components and the second set oflift propulsor components may include two lift propulsor componentseach, wherein the first set of lift propulsor components and a secondset of lift propulsor components are distinct from one another. Forexample, and without limitation, the first set of lift propulsorcomponents may include two lift propulsor components that rotate in aclockwise direction, wherein the second set of lift propulsor componentsmay include two lift propulsor components that rotate in acounterclockwise direction. In an embodiment, and without limitation,the first set of propulsor lift components may be oriented along a lineoriented 45° from the longitudinal axis of aircraft 1600. In anotherembodiment, and without limitation, the second set of propulsor liftcomponents may be oriented along a line oriented 135° from thelongitudinal axis, wherein the first set of lift propulsor componentsline and the second set of lift propulsor components are perpendicularto each other.

Still referring to FIG. 16 , plurality of flight components 1608 mayinclude a pusher component 1616. As used in this disclosure a “pushercomponent” is a component that pushes and/or thrusts an aircraft througha medium. As a non-limiting example, pusher component 1616 may include apusher propeller, a paddle wheel, a pusher motor, a pusher propulsor,and the like. Additionally, or alternatively, pusher flight componentmay include a plurality of pusher flight components. Pusher component1616 is configured to produce a forward thrust. As used in thisdisclosure a “forward thrust” is a thrust that forces aircraft through amedium in a horizontal direction, wherein a horizontal direction is adirection parallel to the longitudinal axis. As a non-limiting example,forward thrust may include a force of 1145 N to force aircraft to in ahorizontal direction along the longitudinal axis. As a furthernon-limiting example, forward thrust may include a force of, as anon-limiting example, 300 N to force aircraft 1600 in a horizontaldirection along a longitudinal axis. As a further non-limiting example,pusher component 1616 may twist and/or rotate to pull air behind it and,at the same time, push aircraft 1600 forward with an equal amount offorce. In an embodiment, and without limitation, the more air forcedbehind aircraft, the greater the thrust force with which the aircraft ispushed horizontally will be. In another embodiment, and withoutlimitation, forward thrust may force aircraft 1600 through the medium ofrelative air. Additionally or alternatively, plurality of flightcomponents 1608 may include one or more puller components. As used inthis disclosure a “puller component” is a component that pulls and/ortows an aircraft through a medium. As a non-limiting example, pullercomponent may include a flight component such as a puller propeller, apuller motor, a tractor propeller, a puller propulsor, and the like.Additionally, or alternatively, puller component may include a pluralityof puller flight components.

In an embodiment and still referring to FIG. 16 , aircraft 1600 mayinclude a flight controller located within fuselage 1604, wherein aflight controller is described in detail below, in reference to FIG. 16. In an embodiment, and without limitation, flight controller may beconfigured to operate a fixed-wing flight capability. As used in thisdisclosure a “fixed-wing flight capability” is a method of flightwherein the plurality of laterally extending elements generate lift. Forexample, and without limitation, fixed-wing flight capability maygenerate lift as a function of an airspeed of aircraft 100 and one ormore airfoil shapes of the laterally extending elements, wherein anairfoil is described above in detail. As a further non-limiting example,flight controller may operate the fixed-wing flight capability as afunction of reducing applied torque on lift propulsor component 1612.For example, and without limitation, flight controller may reduce atorque of 19 Nm applied to a first set of lift propulsor components to atorque of 16 Nm. As a further non-limiting example, flight controllermay reduce a torque of 12 Nm applied to a first set of lift propulsorcomponents to a torque of 0 Nm. In an embodiment, and withoutlimitation, flight controller may produce fixed-wing flight capabilityas a function of increasing forward thrust exerted by pusher component1616. For example, and without limitation, flight controller mayincrease a forward thrust of 1600 kN produced by pusher component 1616to a forward thrust of 1669 kN. In an embodiment, and withoutlimitation, an amount of lift generation may be related to an amount offorward thrust generated to increase airspeed velocity, wherein theamount of lift generation may be directly proportional to the amount offorward thrust produced. Additionally or alternatively, flightcontroller may include an inertia compensator. As used in thisdisclosure an “inertia compensator” is one or more computing devices,electrical components, logic circuits, processors, and the like there ofthat are configured to compensate for inertia in one or more liftpropulsor components present in aircraft 1600. Inertia compensator mayalternatively or additionally include any computing device used as aninertia compensator as described in U.S. Nonprovisional application Ser.No. 17/106,557, filed on Nov. 30, 2020, and entitled “SYSTEM AND METHODFOR FLIGHT CONTROL IN ELECTRIC AIRCRAFT,” the entirety of which isincorporated herein by reference.

In an embodiment, and still referring to FIG. 16 , flight controller maybe configured to perform a reverse thrust command. As used in thisdisclosure a “reverse thrust command” is a command to perform a thrustthat forces a medium towards the relative air opposing aircraft 190. Forexample, reverse thrust command may include a thrust of 180 N directedtowards the nose of aircraft to at least repel and/or oppose therelative air. Reverse thrust command may alternatively or additionallyinclude any reverse thrust command as described in U.S. Nonprovisionalapplication Ser. No. 17/319,155, filed on May 13, 2021, and entitled“AIRCRAFT HAVING REVERSE THRUST CAPABILITIES,” the entirety of which isincorporated herein by reference. In another embodiment, flightcontroller may be configured to perform a regenerative drag operation.As used in this disclosure a “regenerative drag operation” is anoperating condition of an aircraft, wherein the aircraft has a negativethrust and/or is reducing in airspeed velocity. For example, and withoutlimitation, regenerative drag operation may include a positive propellerspeed and a negative propeller thrust. Regenerative drag operation mayalternatively or additionally include any regenerative drag operation asdescribed in U.S. Nonprovisional application Ser. No. 17/319,155.

In an embodiment, and still referring to FIG. 16 , flight controller maybe configured to perform a corrective action as a function of a failureevent. As used in this disclosure a″ corrective action” is an actionconducted by the plurality of flight components to correct and/or altera movement of an aircraft. For example, and without limitation, acorrective action may include an action to reduce a yaw torque generatedby a failure event. Additionally or alternatively, corrective action mayinclude any corrective action as described in U.S. Nonprovisionalapplication Ser. No. 17/222,539, filed on Apr. 7, 2021, and entitled“AIRCRAFT FOR SELF-NEUTRALIZING FLIGHT,” the entirety of which isincorporated herein by reference. As used in this disclosure a “failureevent” is a failure of a lift propulsor component of the plurality oflift propulsor components. For example, and without limitation, afailure event may denote a rotation degradation of a rotor, a reducedtorque of a rotor, and the like thereof.

Now referring to FIG. 17 , an exemplary embodiment 1700 of a flightcontroller 1704 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 1704 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 1704may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 1704 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 17 , flight controller1704 may include a signal transformation component 1708. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 1708may be configured to perform one or more operations such aspreprocessing, lexical analysis, parsing, semantic analysis, and thelike thereof. In an embodiment, and without limitation, signaltransformation component 1708 may include one or more analog-to-digitalconvertors that transform a first signal of an analog signal to a secondsignal of a digital signal. For example, and without limitation, ananalog-to-digital converter may convert an analog input signal to a19-bit binary digital representation of that signal. In anotherembodiment, signal transformation component 1708 may includetransforming one or more low-level languages such as, but not limitedto, machine languages and/or assembly languages. For example, andwithout limitation, signal transformation component 1708 may includetransforming a binary language signal to an assembly language signal. Inan embodiment, and without limitation, signal transformation component1708 may include transforming one or more high-level languages and/orformal languages such as but not limited to alphabets, strings, and/orlanguages. For example, and without limitation, high-level languages mayinclude one or more system languages, scripting languages,domain-specific languages, visual languages, esoteric languages, and thelike thereof. As a further non-limiting example, high-level languagesmay include one or more algebraic formula languages, business datalanguages, string and list languages, object-oriented languages, and thelike thereof.

Still referring to FIG. 17 , signal transformation component 1708 may beconfigured to optimize an intermediate representation 1712. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 1708 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 1708 may optimizeintermediate representation 1712 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 1708 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 1708 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 1704. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 1708 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 17 , flight controller1704 may include a reconfigurable hardware platform 1716. A“reconfigurable hardware platform,” as used herein, is a componentand/or unit of hardware that may be reprogrammed, such that, forinstance, a data path between elements such as logic gates or otherdigital circuit elements may be modified to change an algorithm, state,logical sequence, or the like of the component and/or unit. This may beaccomplished with such flexible high-speed computing fabrics asfield-programmable gate arrays (FPGAs), which may include a grid ofinterconnected logic gates, connections between which may be severedand/or restored to program in modified logic. Reconfigurable hardwareplatform 1716 may be reconfigured to enact any algorithm and/oralgorithm selection process received from another computing deviceand/or created using machine-learning processes.

Still referring to FIG. 17 , reconfigurable hardware platform 1716 mayinclude a logic component 1720. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 1720 may include any suitable processor, suchas without limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 1720 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 1720 mayinclude, incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 1720 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 1720 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 1712. Logiccomponent 1720 may be configured to fetch and/or retrieve theinstruction from a memory cache, wherein a “memory cache,” as used inthis disclosure, is a stored instruction set on flight controller 1704.Logic component 1720 may be configured to decode the instructionretrieved from the memory cache to opcodes and/or operands. Logiccomponent 1720 may be configured to execute the instruction onintermediate representation 1712 and/or output language. For example,and without limitation, logic component 1720 may be configured toexecute an addition operation on intermediate representation 1712 and/oroutput language.

In an embodiment, and without limitation, logic component 1720 may beconfigured to calculate a flight element 1724. As used in thisdisclosure a “flight element” is an element of datum denoting a relativestatus of aircraft. For example, and without limitation, flight element1724 may denote one or more torques, thrusts, airspeed velocities,forces, altitudes, groundspeed velocities, directions during flight,directions facing, forces, orientations, and the like thereof. Forexample, and without limitation, flight element 1724 may denote thataircraft is cruising at an altitude and/or with a sufficient magnitudeof forward thrust. As a further non-limiting example, flight status maydenote that is building thrust and/or groundspeed velocity inpreparation for a takeoff. As a further non-limiting example, flightelement 1724 may denote that aircraft is following a flight pathaccurately and/or sufficiently.

Still referring to FIG. 17 , flight controller 1704 may include achipset component 1728. As used in this disclosure a “chipset component”is a component that manages data flow. In an embodiment, and withoutlimitation, chipset component 1728 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 1720 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 1728 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 1720 to lower-speed peripheral buses, such asa peripheral component interconnect (PCI), industry standardarchitecture (ICA), and the like thereof. In an embodiment, and withoutlimitation, southbridge data flow path may include managing data flowbetween peripheral connections such as ethernet, USB, audio devices, andthe like thereof. Additionally or alternatively, chipset component 1728may manage data flow between logic component 1720, memory cache, and aflight component. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component may include acomponent used to affect the aircraft's roll and pitch which maycomprise one or more ailerons. As a further example, flight componentmay include a rudder to control yaw of an aircraft. In an embodiment,chipset component 1728 may be configured to communicate with a pluralityof flight components as a function of flight element 1724. For example,and without limitation, chipset component 1728 may transmit to anaircraft rotor to reduce torque of a first lift propulsor and increasethe forward thrust produced by a pusher component to perform a flightmaneuver.

In an embodiment, and still referring to FIG. 17 , flight controller1704 may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 1704 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 1724. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 1704 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 1704 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 17 , flight controller1704 may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 1724 and a pilot signal1736 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 1736may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 1736 may include an implicit signal and/oran explicit signal. For example, and without limitation, pilot signal1736 may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 1736 may include an explicitsignal directing flight controller 1704 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 1736 may include an implicit signal, wherein flight controller1704 detects a lack of control such as by a malfunction, torquealteration, flight path deviation, and the like thereof. In anembodiment, and without limitation, pilot signal 1736 may include one ormore explicit signals to reduce torque, and/or one or more implicitsignals that torque may be reduced due to reduction of airspeedvelocity. In an embodiment, and without limitation, pilot signal 1736may include one or more local and/or global signals. For example, andwithout limitation, pilot signal 1736 may include a local signal that istransmitted by a pilot and/or crew member. As a further non-limitingexample, pilot signal 1736 may include a global signal that istransmitted by air traffic control and/or one or more remote users thatare in communication with the pilot of aircraft. In an embodiment, pilotsignal 1736 may be received as a function of a tri-state bus and/ormultiplexor that denotes an explicit pilot signal should be transmittedprior to any implicit or global pilot signal.

Still referring to FIG. 17 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller 1704 and/or a remote device may or may not use inthe generation of autonomous function. As used in this disclosure“remote device” is an external device to flight controller 1704.Additionally or alternatively, autonomous machine-learning model mayinclude one or more autonomous machine-learning processes that afield-programmable gate array (FPGA) may or may not use in thegeneration of autonomous function. Autonomous machine-learning processmay include, without limitation machine learning processes such assimple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naïve bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

In an embodiment, and still referring to FIG. 17 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 1704 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 17 , flight controller 1704 may receiveautonomous machine-learning model from a remote device and/or FPGA thatutilizes one or more autonomous machine learning processes, wherein aremote device and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 1704. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 1704 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 1704 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 17 , flight controller 1704 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 17 , flight controller1704 may include, but is not limited to, for example, a cluster offlight controllers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller1704 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 1704 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 1704 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Mass., USA. In an embodiment, and without limitation,control algorithm may be configured to generate an auto-code, wherein an“auto-code,” is used herein, is a code and/or algorithm that isgenerated as a function of the one or more models and/or software's. Inanother embodiment, control algorithm may be configured to produce asegmented control algorithm. As used in this disclosure a “segmentedcontrol algorithm” is control algorithm that has been separated and/orparsed into discrete sections. For example, and without limitation,segmented control algorithm may parse control algorithm into two or moresegments, wherein each segment of control algorithm may be performed byone or more flight controllers operating on distinct flight components.

In an embodiment, and still referring to FIG. 17 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 17 , the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 1704. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 1712 and/or output language from logiccomponent 1720, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 17 , master bus controller may communicate witha slave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 17 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 17 , flight controller 1704 may also beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofaircraft and/or computing device. Flight controller 1704 may include adistributer flight controller. As used in this disclosure a “distributerflight controller” is a component that adjusts and/or controls aplurality of flight components as a function of a plurality of flightcontrollers. For example, distributer flight controller may include aflight controller that communicates with a plurality of additionalflight controllers and/or clusters of flight controllers. In anembodiment, distributed flight control may include one or more neuralnetworks. For example, neural network also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Still referring to FIG. 17 , a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 17 , flight controller may include asub-controller 1740. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 1704 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller1740 may include any controllers and/or components thereof that aresimilar to distributed flight controller and/or flight controller asdescribed above. Sub-controller 1740 may include any component of anyflight controller as described above. Sub-controller 1740 may beimplemented in any manner suitable for implementation of a flightcontroller as described above. As a further non-limiting example,sub-controller 1740 may include one or more processors, logic componentsand/or computing devices capable of receiving, processing, and/ortransmitting data across the distributed flight controller as describedabove. As a further non-limiting example, sub-controller 1740 mayinclude a controller that receives a signal from a first flightcontroller and/or first distributed flight controller component andtransmits the signal to a plurality of additional sub-controllers and/orflight components.

Still referring to FIG. 17 , flight controller may include aco-controller 1744. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller 1704 ascomponents and/or nodes of a distributer flight controller as describedabove. For example, and without limitation, co-controller 1744 mayinclude one or more controllers and/or components that are similar toflight controller 1704. As a further non-limiting example, co-controller1744 may include any controller and/or component that joins flightcontroller 1704 to distributer flight controller. As a furthernon-limiting example, co-controller 1744 may include one or moreprocessors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data to and/or from flightcontroller 1704 to distributed flight control system. Co-controller 1744may include any component of any flight controller as described above.Co-controller 1744 may be implemented in any manner suitable forimplementation of a flight controller as described above.

In an embodiment, and with continued reference to FIG. 17 , flightcontroller 1704 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 1704 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 18 , an exemplary embodiment of a machine-learningmodule 1800 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 1804 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 1808 given data provided as inputs1812; this is in contrast to a non-machine learning software programwhere the commands to be executed are determined in advance by a userand written in a programming language.

Still referring to FIG. 18 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 1804 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 1804 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 1804 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 1804 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 1804 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 1804 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data1804 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 18 ,training data 1804 may include one or more elements that are notcategorized; that is, training data 1804 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 1804 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 1804 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 1804 used by machine-learning module 1800 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample, aircraft component state data 108, payload data 112, pilot data116, and consumer tool may be inputs and an aircraft performance modeloutput 136 may be an output. In another non-limiting embodiment,schedule database 200 may send an element of data used as an input andoutput an aircraft performance model output 136 via the machine-learningmodel].

Further referring to FIG. 18 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 1816. Training data classifier 1816 may include a“classifier,” which as used in this disclosure is a machine-learningmodel as defined below, such as a mathematical model, neural net, orprogram generated by a machine learning algorithm known as a“classification algorithm,” as described in further detail below, thatsorts inputs into categories or bins of data, outputting the categoriesor bins of data and/or labels associated therewith. A classifier may beconfigured to output at least a datum that labels or otherwiseidentifies a set of data that are clustered together, found to be closeunder a distance metric as described below, or the like.Machine-learning module 1800 may generate a classifier using aclassification algorithm, defined as a processes whereby a computingdevice and/or any module and/or component operating thereon derives aclassifier from training data 1804. Classification may be performedusing, without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers. As a non-limiting example, training dataclassifier 1816 may classify elements of training data to [such as acohort of user, flight manager, owners, and the like thereof. Trainingdata classifier 1816 may classify elements of training data such as typeof flight issue, complaint, user experience, and/or other analyzed itemsand/or phenomena for which a subset of training data may be selected].

Still referring to FIG. 18 , machine-learning module 1800 may beconfigured to perform a lazy-learning process 1820 and/or protocol,which may alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 1804.Heuristic may include selecting some number of highest-rankingassociations and/or training data 1804 elements. Lazy learning mayimplement any suitable lazy learning algorithm, including withoutlimitation a K-nearest neighbors algorithm, a lazy naïve Bayesalgorithm, or the like; persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various lazy-learningalgorithms that may be applied to generate outputs as described in thisdisclosure, including without limitation lazy learning applications ofmachine-learning algorithms as described in further detail below.

Alternatively or additionally, and with continued reference to FIG. 18 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 1824. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 1824 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 1824 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 1804set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 18 , machine-learning algorithms may include atleast a supervised machine-learning process 1828. At least a supervisedmachine-learning process 1828, as defined herein, include algorithmsthat receive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude inputs as described in this disclosure and outputs as describedin this disclosure, and a scoring function representing a desired formof relationship to be detected between inputs and outputs; scoringfunction may, for instance, seek to maximize the probability that agiven input and/or combination of elements inputs is associated with agiven output to minimize the probability that a given input is notassociated with a given output. Scoring function may be expressed as arisk function representing an “expected loss” of an algorithm relatinginputs to outputs, where loss is computed as an error functionrepresenting a degree to which a prediction generated by the relation isincorrect when compared to a given input-output pair provided intraining data 1804. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various possiblevariations of at least a supervised machine-learning process 1828 thatmay be used to determine relation between inputs and outputs. Supervisedmachine-learning processes may include classification algorithms asdefined above.

Further referring to FIG. 18 , machine learning processes may include atleast an unsupervised machine-learning processes 1832. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 18 , machine-learning module 1800 may bedesigned and configured to create a machine-learning model 1824 usingtechniques for development of linear regression models. Linearregression models may include ordinary least squares regression, whichaims to minimize the square of the difference between predicted outcomesand actual outcomes according to an appropriate norm for measuring sucha difference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 18 , machine-learning algorithms mayinclude, without limitation, linear discriminant analysis.Machine-learning algorithm may include quadratic discriminate analysis.Machine-learning algorithms may include kernel ridge regression.Machine-learning algorithms may include support vector machines,including without limitation support vector classification-basedregression processes. Machine-learning algorithms may include stochasticgradient descent algorithms, including classification and regressionalgorithms based on stochastic gradient descent. Machine-learningalgorithms may include nearest neighbors algorithms. Machine-learningalgorithms may include various forms of latent space regularization suchas variational regularization. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 19 , an exemplary embodiment of a system 1900 forelectric aircraft fleet management is illustrated. System includes acomputing device. Computing device may include any computing device asdescribed in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Computing devicemay include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. Computing device may include asingle computing device operating independently, or may include two ormore computing device operating in concert, in parallel, sequentially orthe like; two or more computing devices may be included together in asingle computing device or in two or more computing devices. Computingdevice may interface or communicate with one or more additional devicesas described below in further detail via a network interface device.Network interface device may be utilized for connecting computing deviceto one or more of a variety of networks, and one or more devices.Examples of a network interface device include, but are not limited to,a network interface card (e.g., a mobile network interface card, a LANcard), a modem, and any combination thereof. Examples of a networkinclude, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.Computing device may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Computing device may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Computing device may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Computing device may be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of system 100and/or computing device.

With continued reference to FIG. 1 , a flight controller 120 include acomputing device may be designed and/or configured to perform anymethod, method step, or sequence of method steps in any embodimentdescribed in this disclosure, in any order and with any degree ofrepetition. For instance, [computing device] may be configured toperform a single step or sequence repeatedly until a desired orcommanded outcome is achieved; repetition of a step or a sequence ofsteps may be performed iteratively and/or recursively using outputs ofprevious repetitions as inputs to subsequent repetitions, aggregatinginputs and/or outputs of repetitions to produce an aggregate result,reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Computing device mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 19 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 1900 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 1900 includes a processor 1904 and a memory1908 that communicate with each other, and with other components, via abus 1912. Bus 1912 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 1904 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 1904 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 1904 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 1908 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 1916 (BIOS), including basic routines thathelp to transfer information between elements within computer system1900, such as during start-up, may be stored in memory 1908. Memory 1908may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 1920 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 1908 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 1900 may also include a storage device 1924. Examples ofa storage device (e.g., storage device 1924) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 1924 may beconnected to bus 1912 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device1924 (or one or more components thereof) may be removably interfacedwith computer system 1900 (e.g., via an external port connector (notshown)). Particularly, storage device 1924 and an associatedmachine-readable medium 1928 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 1900. In one example,software 1920 may reside, completely or partially, withinmachine-readable medium 1928. In another example, software 1920 mayreside, completely or partially, within processor 1904.

Computer system 1900 may also include an input device 1932. In oneexample, a user of computer system 1900 may enter commands and/or otherinformation into computer system 1900 via input device 1932. Examples ofan input device 1932 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 1932may be interfaced to bus 1912 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 1912, and any combinations thereof. Input device 1932may include a touch screen interface that may be a part of or separatefrom display 1936, discussed further below. Input device 1932 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 1900 via storage device 1924 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 1040. A networkinterface device, such as network interface device 1040, may be utilizedfor connecting computer system 1900 to one or more of a variety ofnetworks, such as network 1044, and one or more remote devices 1048connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 1044, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 1920, etc.) may be communicated to and/or fromcomputer system 1900 via network interface device 1040.

Computer system 1900 may further include a video display adapter 1952for communicating a displayable image to a display device, such asdisplay device 1936. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 1952 and display device 1936 maybe utilized in combination with processor 1904 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 1900 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 1912 via a peripheral interface 1956.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve the systemsaccording to the present disclosure. Accordingly, this description ismeant to be taken only by way of example, and not to otherwise limit thescope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for electric aircraft fleet management,the system comprising: a computing device communicatively connected toat least an electric aircraft, wherein the computing device is remote tothe at least the electric aircraft and configured to: authenticate theat least an aircraft using a credential received from the at least anelectric aircraft; receive a plurality of measured aircraft operationdatum from a sensor disposed on the at least an electric aircraft;select a training set as a function of each measured aircraft operationdatum of the plurality of measured aircraft operation datum and the atleast an electric aircraft, wherein each measured aircraft operationdatum of the plurality of measured aircraft operation datum iscorrelated to a health history of at least a flight component, whereinthe health history comprises the health of the at least a flightcomponent since previous authentication; and generate, using amachine-learning algorithm, an aircraft performance model output basedon the plurality of measured aircraft operation datum and the selectedtraining set, wherein generating the aircraft performance model outputcomprises generating a performance alert.
 2. The system of claim 1,wherein the sensor is disposed on the at least a flight component of theat least an electric aircraft.
 3. The system of claim 1, whereingenerating the aircraft performance model output comprises derivingindividual functions describing unique correlations between aircraftperformance model output training data for each aircraft component statedata.
 4. The system of claim 1, wherein the computing device is furtherconfigured to generate the aircraft performance model output using asupervised machine-learning algorithm based on the plurality of measuredaircraft operation datum and the selected training set.
 5. The system ofclaim 1, wherein the performance model output comprises a virtualrepresentation.
 6. The system of claim 5, wherein the virtualrepresentation comprises a video simulation.
 7. The system of claim 1,wherein generating the performance alert comprises generating theperformance alert as a function of a flight plan.
 8. The system of claim1, wherein the authenticating the at least an electric aircraftcomprises: receiving a credential from the at least an electricaircraft; comparing the credential to an authorized credential storedwithin an authentication database; and bypassing authentication for theat least an electric aircraft based on the comparison of the receivedcredential from the at least an aircraft to the authorized credentialstored within the authentication database.
 9. The system of claim 1,wherein the plurality of measured aircraft operation datum furthercomprises a plurality of component state data.
 10. The system of claim1, wherein the computing device is further configured to transmit theperformance alert to a user device.
 11. The system of claim 10, whereinthe performance alert comprises a warning sign displayed on the userdevice.
 12. A method for electric aircraft fleet management, the methodcomprising: authenticating, at a computing device remote to at leastelectric aircraft and communicatively connected to the at least anelectric aircraft, the at least an aircraft using a credential receivedfrom the at least an electric aircraft; receiving, at the computingdevice, a plurality of measured aircraft operation datum from a sensor;selecting, at the computing device, a training set as a function of eachmeasured aircraft operation datum of the plurality of measured aircraftoperation datum and the at least an electric aircraft, wherein eachmeasured aircraft operation datum of the plurality of measured aircraftoperation datum is correlated a health history of at least a flightcomponent, wherein the health history comprises the health of the atleast a flight component since previous authentication; and generating,using a machine-learning algorithm at the computing device, an aircraftperformance model output based on the plurality of measured aircraftoperation datum and the selected training set, wherein generating theaircraft performance model comprises generating a performance alert. 13.The method of claim 12, wherein the sensor is disposed on the at least aflight component of the at least an electric aircraft.
 14. The method ofclaim 12, wherein generating the aircraft performance model outputcomprises deriving individual functions describing unique correlationsbetween aircraft performance model output training data for eachaircraft component state data.
 15. The method of claim 12, furthercomprising generating, at the computing device, the aircraft performancemodel output using a supervised machine-learning algorithm based on theplurality of measured aircraft operation datum and the selected trainingset.
 16. The method of claim 12, wherein the performance model outputcomprises a virtual representation.
 17. The method of claim 15, whereinthe virtual representation comprises a video simulation.
 18. The methodof claim 12, wherein authenticating the at least an electric aircraftcomprises: receiving a credential from the at least an electricaircraft; comparing the credential to an authorized credential storedwithin an authentication database; and bypassing authentication for theat least an electric aircraft based on the comparison of the receivedcredential from the at least an aircraft to the authorized credentialstored within the authentication database.
 19. The method of claim 12,wherein the plurality of measured aircraft operation datum furthercomprises a plurality of component state data.
 20. The method of claim12, further comprising transmitting, at the computing device, theperformance alert to a user device.